AI Tools to Extract Figures, Tables, and Citations from Papers

Q: Can ChatGPT extract tables from a PDF research paper?

Yes, with the right approach. Upload the PDF directly in ChatGPT (GPT-4o supports file uploads) or paste extracted text from the PDF into the prompt. Specify that you want a Markdown or CSV table and name the columns you expect. Results are good for well-formatted digital PDFs but degrade for scanned documents.

Q: What is the best free tool to extract data from a research paper PDF?

For structured extraction, combining pdfplumber (free Python library) for text extraction with a free-tier API call to Claude or GPT-4o-mini gives strong results at low cost. For references specifically, GROBID is a free open-source tool purpose-built for parsing academic paper metadata and citation lists.

Q: How do I extract citations from a PDF and convert them to BibTeX?

Paste the reference list text into an AI prompt and ask it to return BibTeX entries with a specific key format. Alternatively, use GROBID or tools like Anystyle.io which are trained specifically on reference parsing. For DOI-linked papers, the CrossRef API can return clean BibTeX if you provide the DOI.

Q: Can AI extract data from figures and charts in research papers?

AI can read and summarize figure captions accurately. However, extracting numeric data points from chart images (bar heights, scatter plot coordinates, line values) is unreliable even with vision models. For that task, use WebPlotDigitizer, which lets you manually calibrate axes and extract data points precisely.

Q: How do I extract data from multiple research papers at once?

Build a simple script using Python with pdfplumber or PyMuPDF to extract text from each PDF, then loop through them calling an LLM API (OpenAI, Anthropic, etc.) with your extraction prompt. Structure the outputs into a JSON or CSV as you go. For very large batches, tools like Elicit or Consensus are built specifically for multi-paper literature extraction.

Q: Why does extracted text from my research paper PDF look scrambled?

Most likely your PDF uses a two-column layout, which standard PDF text extractors read left-to-right across both columns, mixing content. Use pdfplumber with bounding box coordinates to extract each column separately, or try Adobe Acrobat's export feature which handles multi-column layouts better than most open-source tools.

Tested prompts for extract data from research paper pdf compared across 5 leading AI models.

BEST BY JUDGE SCORE Claude Haiku 4.5 8/10

Research PDFs are notoriously hard to work with. Tables get flattened into unreadable text, figure captions separate from their images, and citation lists become one long unbroken string. If you are trying to pull structured data out of a paper, whether that is a results table, a list of references, or the numeric values inside a figure, copy-pasting from a PDF almost never works cleanly.

AI models have changed this significantly. By feeding a paper into a model with a precise extraction prompt, you can get back structured outputs: JSON objects, Markdown tables, BibTeX citations, or plain numbered lists, depending on what you need. The key is knowing how to prompt correctly and which model handles which extraction type best.

This page shows you a tested prompt, four model outputs side by side, and a comparison table so you can pick the right tool for your specific extraction task. Whether you are a researcher building a literature database, an analyst pulling trial data, or a developer ingesting paper metadata at scale, the workflow below covers your case.

When to use this

This approach works best when you have a PDF of a research paper and need to get structured, reusable data out of it fast. It fits situations where manual copy-paste is too slow, too error-prone, or simply does not preserve structure. It is especially useful when you need the same extraction repeated across multiple papers.

Pulling numerical results from a results or performance comparison table to feed into your own spreadsheet or database
Extracting all citations from a paper's reference section and formatting them as BibTeX or APA for a literature review
Collecting figure captions and their associated descriptions to build a visual index of a paper corpus
Scraping author affiliations, DOIs, publication dates, and keywords from paper headers for metadata pipelines
Extracting methodology details such as dataset names, sample sizes, and model hyperparameters for reproducibility tracking

When this format breaks down

Scanned PDFs without OCR: if the PDF is an image scan with no text layer, most AI models cannot read it without a dedicated OCR step first. Run it through a tool like Adobe Acrobat OCR or Tesseract before attempting AI extraction.
Papers with complex multi-column layouts and deeply nested tables: columns often get merged or reordered during text extraction, corrupting the data before the AI even sees it. Validate output carefully or use a layout-aware parser like GROBID as a preprocessing step.
Extracting data from figures that are actual charts or graphs: AI can read figure captions, but it cannot reliably read numeric values off a bar chart or scatter plot image. Use a dedicated plot digitizer tool for that.
High-stakes clinical or regulatory use where errors have consequences: AI extraction can hallucinate values or misread superscripts and subscripts in statistical notation. Always have a human verify extracted numbers before using them in any formal analysis.

The prompt we tested

You are a research data extraction specialist. Extract structured information from the research paper content provided below.

Extraction rules:
Extract the following elements: (1) paper metadata (title, authors, year, journal, DOI), (2) all figures with captions and described data, (3) all tables with headers and key values, (4) numerical results and statistics with units, (5) methodology summary, and (6) full citation list. Preserve exact numerical values and units, and use bullet points or tables for clarity. Keep the total output under 800 words unless the source is exceptionally data-dense.

Research paper content:
Title: Effects of Intermittent Fasting on Metabolic Health in Adults: A Randomized Controlled Trial. Published in the Journal of Nutrition, 2023. The study included 240 participants over 12 weeks and measured changes in BMI, fasting glucose, and LDL cholesterol across three dietary conditions, with results shown in Table 2 (mean ± SD) and Figure 3 (weight loss trajectories). Key finding: the 16:8 fasting group showed a 3.2% reduction in body weight (p<0.01) compared to 1.1% in controls.

Return the extracted data in clean, organized Markdown with clear sections for Metadata, Abstract Summary, Figures, Tables, Key Findings, Methodology, and Citations. For each figure and table, include its number, caption, and a brief description of the data it contains. For citations, list them in a numbered reference format. If any section cannot be extracted from the provided content, note it as 'Not available in provided text'.

Model comparison

Model	Quality	Cost	Latency	Tokens
Claude Opus 4.7	8	$0.0885	14.0s	524 / 1075
Claude Haiku 4.5	8	$0.0055	8.2s	372 / 1023
GPT-5.4	8	$0.0135	10.7s	337 / 844
Gemini 2.5 Pro	7	$0.0021	16.4s	341 / 342
Grok 4.1 Fast Reasoning	8	$0.0002	7.3s	480 / 287

Outputs

Claude Opus 4.7 8/10

# Extracted Research Data

## Metadata
- **Title:** Effects of Intermittent Fasting on Metabolic Health in Adults: A Randomized Controlled Trial
- **Authors:** Not available in provided text
- **Year:** 2023
- **Journal:** Journal of Nutrition
- **DOI:** Not available in provided text
- **Study Type:** Randomized Controlled Trial (RCT)

## Abstract Summary
A 12-week randomized controlled trial evaluated the effects of intermittent fasting on metabolic health markers across three dietary conditions in 240 adult participants. Primary outcomes included changes in BMI, fasting glucose, and LDL cholesterol. The 16:8 intermittent fasting protocol produced significantly greater body weight reduction compared to controls.

## Figures

| Figure # | Caption / Description |
|----------|----------------------|
| Figure 3 | **Weight loss trajectories** — Depicts weight change over the 12-week intervention across the three dietary conditions. Full underlying data not available in provided text. |

*Figures 1 and 2: Not available in provided text.*

## Tables

| Table # | Caption / Description |
|---------|----------------------|
| Table 2 | Reports changes in **BMI, fasting glucose, and LDL cholesterol** across the three dietary conditions, presented as **mean ± SD**. Specific numeric cell values not available in provided text. |

*Table 1: Not available in provided text.*

## Key Findings (Numerical Results & Statistics)
- **Sample size:** n = 240 participants
- **Study duration:** 12 weeks
- **Dietary conditions compared:** 3 (including a 16:8 fasting group and a control group)
- **Body weight reduction — 16:8 fasting group:** 3.2% (p < 0.01)
- **Body weight reduction — control group:** 1.1%
- **Between-group difference:** ~2.1 percentage points favoring 16:8 fasting
- **Outcome measures:** BMI, fasting glucose (mg/dL — units not specified in text), LDL cholesterol (mg/dL — units not specified in text)

## Methodology
- **Design:** Randomized controlled trial
- **Participants:** 240 adults
- **Duration:** 12 weeks
- **Intervention arms:** Three dietary conditions, including a 16:8 intermittent fasting protocol and a control group (third condition not specified in provided text)
- **Outcomes measured:** 
  - Body Mass Index (BMI)
  - Fasting glucose
  - LDL cholesterol
  - Body weight change (%)
- **Statistical reporting:** Results presented as mean ± SD; significance testing applied (p-values reported)
- **Additional methodological details** (randomization, blinding, inclusion/exclusion criteria, dietary adherence monitoring): Not available in provided text

## Citations
Not available in provided text.

---
*Note: The source content provided was a brief summary excerpt rather than a full paper. Several standard sections (full author list, abstract, complete table values, additional figures, and reference list) could not be extracted and are marked accordingly.*

Claude Haiku 4.5 8/10

# Research Data Extraction Report

## Metadata

| Field | Details |
|-------|---------|
| **Title** | Effects of Intermittent Fasting on Metabolic Health in Adults: A Randomized Controlled Trial |
| **Journal** | Journal of Nutrition |
| **Year** | 2023 |
| **DOI** | Not available in provided text |
| **Authors** | Not available in provided text |
| **Study Duration** | 12 weeks |
| **Sample Size** | 240 participants |

---

## Abstract Summary

This randomized controlled trial examined the effects of intermittent fasting on metabolic health markers in adults over a 12-week intervention period. The study evaluated three dietary conditions and measured changes in body mass index (BMI), fasting glucose levels, and LDL cholesterol concentration.

---

## Figures

### Figure 3
**Caption:** Weight loss trajectories across three dietary conditions over 12-week intervention period

**Data Description:**
- Displays longitudinal weight loss patterns across study duration
- Compares trajectory between 16:8 fasting group and control group
- 16:8 fasting group demonstrated sustained weight loss progression
- Control group showed minimal weight change over 12 weeks

---

## Tables

### Table 2
**Caption:** Mean ± SD values for metabolic health markers across dietary conditions

**Data Description:**
The table presents baseline and endpoint measurements for the three dietary conditions; specific values not provided in source text but includes:
- Body Mass Index (BMI)
- Fasting glucose levels
- LDL cholesterol concentrations

*Note: Specific numerical values from Table 2 not available in provided text*

---

## Key Findings & Numerical Results

| Outcome | 16:8 Fasting Group | Control Group | Statistical Significance |
|---------|-------------------|---------------|-------------------------|
| **Body Weight Reduction** | 3.2% | 1.1% | p < 0.01 |
| **Study Duration** | 12 weeks | 12 weeks | — |
| **Sample Size** | Not specified | Not specified | — |

### Primary Finding
- **16:8 Intermittent Fasting Protocol:** Achieved a **3.2% reduction in body weight** (p < 0.01) 
- **Control Group:** Demonstrated **1.1% weight reduction**
- **Difference:** The fasting intervention produced approximately **2.1 percentage points greater weight loss** than control conditions

### Additional Metrics Measured
- Fasting glucose levels (specific values not provided)
- LDL cholesterol (specific values not provided)
- BMI changes (specific values not provided)

---

## Methodology Summary

**Study Design:**
- Randomized Controlled Trial (RCT)
- Parallel group design with three dietary conditions
- Duration: 12 weeks

**Participants:**
- Total sample: n = 240 adults
- Demographics: Not available in provided text

**Interventions:**
- Condition 1: 16:8 Intermittent Fasting Protocol (16 hours fasting, 8-hour eating window)
- Condition 2: Control group
- Condition 3: Not specified in provided text

**Measured Outcomes:**
- Body Mass Index (BMI)
- Body weight (% change)
- Fasting glucose concentration
- LDL cholesterol levels

**Statistical Analysis:**
- Mean ± Standard Deviation (SD) reporting
- p-value threshold: p < 0.01 for significance
- Comparative analysis across groups

---

## Citations

Not available in provided text

*Note: The source material provided does not include a reference list. For complete citations, please consult the original published article.*

---

## Data Limitations & Notes

⚠️ **Information Not Available in Provided Text:**
- Complete author names and affiliations
- Digital Object Identifier (DOI)
- Specific mean ± SD values from Table 2
- Detailed metrics for fasting glucose and LDL cholesterol outcomes
- Description of the third dietary condition
- Participant demographic characteristics
- Drop-out/attrition rates
- Complete reference citations
- Detailed methodology sections (inclusion/exclusion criteria, randomization procedure, blinding status)

**Recommendation:** Consult the full published article in the Journal of Nutrition (2023) for comprehensive methodology, complete statistical tables, and full reference list.

GPT-5.4 8/10

# Metadata

- **Title:** Effects of Intermittent Fasting on Metabolic Health in Adults: A Randomized Controlled Trial
- **Authors:** Not available in provided text
- **Year:** 2023
- **Journal:** Journal of Nutrition
- **DOI:** Not available in provided text

# Abstract Summary

- **Study design:** Randomized controlled trial
- **Population:** 240 adult participants
- **Duration:** 12 weeks
- **Interventions/conditions:** Three dietary conditions
- **Outcomes measured:** BMI, fasting glucose, and LDL cholesterol
- **Main finding:** The **16:8 fasting group** showed a **3.2% reduction in body weight** compared with **1.1% in controls**, with **p < 0.01**

# Figures

## Figure 3
- **Caption:** Weight loss trajectories
- **Description of data:** Figure 3 presents weight loss trajectories over the 12-week intervention period across the dietary groups, including the 16:8 fasting group and controls. It is described as showing the pattern of weight change over time.
- **Numerical data explicitly provided in text:**
  - 16:8 fasting group: **3.2% reduction in body weight**
  - Control group: **1.1% reduction in body weight**
  - Statistical significance: **p < 0.01**

# Tables

## Table 2
- **Caption:** Not available in provided text
- **Description of data:** Table 2 reports outcomes as **mean ± SD** for metabolic measures across the three dietary conditions.
- **Variables included:**
  - BMI
  - Fasting glucose
  - LDL cholesterol
- **Column headers:** Not available in provided text
- **Key values:** Exact mean ± SD values not available in provided text

# Key Findings

- **Sample size:** **240 participants**
- **Study duration:** **12 weeks**
- **Body weight change:**
  - 16:8 fasting group: **3.2% reduction**
  - Control group: **1.1% reduction**
  - **Difference statistically significant:** **p < 0.01**
- **Metabolic endpoints assessed:**
  - **BMI**
  - **Fasting glucose**
  - **LDL cholesterol**
- **Data format noted for primary results:** **mean ± SD**

# Numerical Results and Statistics

- **Participants:** **240**
- **Intervention length:** **12 weeks**
- **Body weight reduction, 16:8 fasting group:** **3.2%**
- **Body weight reduction, control group:** **1.1%**
- **p-value for comparison:** **p < 0.01**
- **Other numerical results for BMI, fasting glucose, LDL cholesterol:** Not available in provided text
- **Units explicitly mentioned:**
  - Body weight change: **%**
  - Duration: **weeks**
- **Units not provided for:**
  - BMI
  - Fasting glucose
  - LDL cholesterol

# Methodology

- **Design:** Randomized controlled trial
- **Participants:** Adults
- **Total sample size:** **240**
- **Study length:** **12 weeks**
- **Comparison groups:** **Three dietary conditions**
- **Primary/assessed outcomes:**
  - BMI
  - Fasting glucose
  - LDL cholesterol
  - Body weight change trajectory
- **Statistical presentation:** Results shown in **Table 2 (mean ± SD)** and **Figure 3**
- **Randomization details, eligibility criteria, adherence assessment, and statistical methods:** Not available in provided text

# Citations

1. **Effects of Intermittent Fasting on Metabolic Health in Adults: A Randomized Controlled Trial.** *Journal of Nutrition*. 2023. DOI: Not available in provided text.

# Missing Information Noted

- Authors
- DOI
- Full figure caption text
- Table 2 caption and exact table values
- Detailed statistical methods
- Full citation list beyond the study itself

Gemini 2.5 Pro 7/10

Here is the extracted data from the research paper content.

### **Metadata**
* **Title**: Effects of Intermittent Fasting on Metabolic Health in Adults: A Randomized Controlled Trial.
* **Authors**: Not available in provided text.
* **Year**: 2023
* **Journal**: Journal of Nutrition
* **DOI**: Not available in provided text.

### **Abstract Summary**
This randomized controlled trial investigated the effects of intermittent fasting on metabolic health in 240 adults over a 12-week period. The study compared metabolic outcomes, including BMI, fasting glucose, and LDL cholesterol, across three different dietary groups. The 16:8 intermittent fasting group demonstrated a statistically significant reduction in body weight compared to the control group.

### **Figures**
* **Figure 3**
* **Caption**: Weight loss trajectories.
* **Description**: This figure shows the progression of weight loss over the 12-week study for the different dietary groups. The specific data points and curves are not provided in the text.

### **Tables**
* **Table 2**
* **Caption**: Not explicitly stated. Inferred title: "Changes in Metabolic Markers Across Dietary Conditions."
* **Description**: This table contains the primary results for changes in BMI, fasting glucose, and LDL cholesterol for the three dietary conditions. Data is presented as mean ± standard deviation (SD). Specific values for each group are not provided in the text.

### **Key Findings**
* **Body Weight Reduction**: The 16:8 intermittent fasting group

Grok 4.1 Fast Reasoning 8/10

## Metadata
- **Title**: Effects of Intermittent Fasting on Metabolic Health in Adults: A Randomized Controlled Trial
- **Authors**: Not available in provided text
- **Year**: 2023
- **Journal**: Journal of Nutrition
- **DOI**: Not available in provided text

## Abstract Summary
Not available in provided text (content appears to be a summary snippet).

## Figures
- **Figure 3**: Caption not available. Contains weight loss trajectories across groups.

## Tables
- **Table 2**: Caption not available (mean ± SD). Shows changes in BMI, fasting glucose, and LDL cholesterol across three dietary conditions. Headers and key values not detailed in provided text.

## Key Findings
- 16:8 fasting group: 3.2% reduction in body weight (p<0.01)
- Control group: 1.1% reduction in body weight
- Numerical results: 240 participants; 12 weeks duration; measured BMI, fasting glucose, LDL cholesterol (exact values/units not specified beyond percentages and p-value).

## Methodology
Randomized Controlled Trial (RCT) with 240 participants over 12 weeks. Measured changes in BMI, fasting glucose, and LDL cholesterol across three dietary conditions (including 16:8 intermittent fasting and controls). Results reported as mean ± SD in Table 2 and trajectories in Figure 3.

## Citations
Not available in provided text.

What makes these work

01

Specify your output format explicitly
AI models default to prose if you do not tell them otherwise. Always state the exact format you want: Markdown table, JSON array, BibTeX, CSV. Naming the column headers or JSON keys in your prompt dramatically reduces reformatting work on your end.
02

Extract text before prompting
Most AI chat interfaces do not parse PDFs perfectly on their own. Use a tool like PyMuPDF, pdfplumber, or Adobe Acrobat to extract raw text first, then paste that text into your prompt. This gives the model clean input and reduces layout-related errors.
03

Isolate the section you need
Sending an entire 30-page paper wastes context and introduces noise. Identify the specific section (results, references, methods) and paste only that. Models perform better on focused input and you stay within token limits for most commercial APIs.
04

Ask for null handling explicitly
Research papers frequently omit values, especially in comparison tables. Tell the model what to write when data is missing: N/A, null, or an empty string. Without this instruction, models sometimes hallucinate plausible-looking values to fill gaps.

More example scenarios

#01 · Extracting a model comparison table from an NLP benchmark paper

Input

Here is the text of a results section from a machine learning paper. Extract the model comparison table and return it as a Markdown table with columns: Model, BLEU Score, ROUGE-L, Parameters (B). If a value is missing, write N/A. Text: [paste extracted table text here]

Expected output

| Model | BLEU Score | ROUGE-L | Parameters (B) |
|---|---|---|---|
| GPT-4 | 41.2 | 38.7 | ~1000 |
| LLaMA-2 70B | 38.9 | 36.1 | 70 |
| Mistral 7B | 35.4 | 33.8 | 7 |
| Baseline | 29.1 | 27.4 | N/A |

#02 · Pulling all references from a clinical trial paper into BibTeX

Input

Below is the reference list from a clinical trial PDF. Convert every reference into a BibTeX entry. Use the first author's last name plus year as the citation key. Preserve DOIs where present. References: 1. Smith J, et al. Efficacy of drug X. NEJM. 2021;384:1502-1511. DOI: 10.1056/NEJMoa2034577 ...

Expected output

@article{smith2021,
  author = {Smith, J. and others},
  title = {Efficacy of drug X},
  journal = {New England Journal of Medicine},
  year = {2021},
  volume = {384},
  pages = {1502--1511},
  doi = {10.1056/NEJMoa2034577}
}

#03 · Extracting dataset metadata from a computer vision paper

Input

Read this methods section and extract every dataset mentioned. For each dataset return: dataset name, number of samples, image resolution if stated, and the task it was used for. Format as a JSON array. Text: [methods section text]

Expected output

[{"dataset": "ImageNet", "samples": 1280000, "resolution": "224x224", "task": "pretraining"}, {"dataset": "COCO", "samples": 118000, "resolution": null, "task": "object detection fine-tuning"}]

#04 · Extracting author and affiliation metadata for a research database

Input

Extract the author list and affiliations from this paper header. Return a JSON array where each object has: full_name, affiliation, country, and email if listed. Header text: John A. Doe1, Maria L. Chen2 -- 1. MIT, Cambridge, USA, jdoe@mit.edu -- 2. University of Tokyo, Japan

Expected output

[{"full_name": "John A. Doe", "affiliation": "MIT", "country": "USA", "email": "jdoe@mit.edu"}, {"full_name": "Maria L. Chen", "affiliation": "University of Tokyo", "country": "Japan", "email": null}]

#05 · Extracting statistical outcomes from a psychology meta-analysis

Input

From this results section, extract every reported effect size and p-value. For each, record: construct measured, effect size type (Cohen's d, r, etc.), value, sample size N, and p-value. Return as a CSV with headers. Text: [results section]

Expected output

construct,effect_size_type,value,N,p_value
Anxiety reduction,Cohen's d,0.72,143,0.003
Depression symptoms,r,0.41,98,0.01
Quality of life,Cohen's d,0.55,143,0.021

Common mistakes to avoid

Trusting numeric values without verification
AI models can misread subscripts, superscripts, and hyphenated ranges in statistical tables. A p-value of 0.001 can become 0.01 silently. Always cross-check extracted numbers against the original PDF before using them in analysis or publication.
Feeding a scanned image PDF directly
If your PDF was created by scanning a physical paper, it has no text layer and the model is effectively reading nothing or seeing a blank document. Run OCR first. Skipping this step produces empty or hallucinated output with no error message to warn you.
Not specifying citation format
Asking a model to 'extract references' without specifying BibTeX, APA, MLA, or JSON will return a mix of styles or a numbered list with inconsistent formatting. Downstream tools like Zotero or LaTeX will reject inconsistently formatted references, costing you cleanup time.
Sending multi-column PDFs as raw text
Two-column academic papers, when extracted as plain text, interleave the two columns line by line, scrambling sentences across columns. The model then extracts garbled data. Use a column-aware PDF parser or manually separate the columns before passing text to the model.
Expecting figures to be readable as data
Asking the model to extract data from a bar chart or line graph image embedded in the PDF will fail or return estimated guesses. AI vision models can describe a chart but cannot reliably digitize its values. Use WebPlotDigitizer or a similar tool for chart data extraction.

Related queries

Frequently asked questions

Can ChatGPT extract tables from a PDF research paper?

Yes, with the right approach. Upload the PDF directly in ChatGPT (GPT-4o supports file uploads) or paste extracted text from the PDF into the prompt. Specify that you want a Markdown or CSV table and name the columns you expect. Results are good for well-formatted digital PDFs but degrade for scanned documents.

What is the best free tool to extract data from a research paper PDF?

For structured extraction, combining pdfplumber (free Python library) for text extraction with a free-tier API call to Claude or GPT-4o-mini gives strong results at low cost. For references specifically, GROBID is a free open-source tool purpose-built for parsing academic paper metadata and citation lists.

How do I extract citations from a PDF and convert them to BibTeX?

Paste the reference list text into an AI prompt and ask it to return BibTeX entries with a specific key format. Alternatively, use GROBID or tools like Anystyle.io which are trained specifically on reference parsing. For DOI-linked papers, the CrossRef API can return clean BibTeX if you provide the DOI.

Can AI extract data from figures and charts in research papers?

AI can read and summarize figure captions accurately. However, extracting numeric data points from chart images (bar heights, scatter plot coordinates, line values) is unreliable even with vision models. For that task, use WebPlotDigitizer, which lets you manually calibrate axes and extract data points precisely.

How do I extract data from multiple research papers at once?

Build a simple script using Python with pdfplumber or PyMuPDF to extract text from each PDF, then loop through them calling an LLM API (OpenAI, Anthropic, etc.) with your extraction prompt. Structure the outputs into a JSON or CSV as you go. For very large batches, tools like Elicit or Consensus are built specifically for multi-paper literature extraction.

Why does extracted text from my research paper PDF look scrambled?

Most likely your PDF uses a two-column layout, which standard PDF text extractors read left-to-right across both columns, mixing content. Use pdfplumber with bounding box coordinates to extract each column separately, or try Adobe Acrobat's export feature which handles multi-column layouts better than most open-source tools.

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