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pretty_name: Paper2Thesis
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
---
# Paper2Thesis
> **Anonymized release for NeurIPS 2026 Datasets and Benchmarks Track review.**
> The non-anonymous version, including author information and a permanent DOI, will be released upon acceptance.
## Overview
Paper2Thesis is a benchmark for **extreme-length multi-document synthesis**. Each instance maps a set of input arXiv research papers to a target arXiv PhD thesis. The task requires generating a thesis-scale document that integrates multiple papers into a coherent, structured narrative.
This benchmark targets a regime beyond standard text generation, involving **long-context reasoning**, **cross-document integration**, and **structured generation**.
---
## Task
Each example is defined as:
[input_paper_ids] → target_thesis_id
The objective is to synthesize the input papers into a unified thesis-level document. Unlike summarization, the task requires **expansion**, **restructuring**, and **integration** across multiple sources.
---
## Dataset Structure
The dataset is provided in JSONL format:
paper2thesis/
data/
train.jsonl
validation.jsonl
test.jsonl
Each line in a JSONL file corresponds to one example.
Example:
{
"example_id": "p2t_000033",
"input_paper_ids": ["2208.09377", "2503.22525", "2405.08703"],
"target_thesis_id": "2508.07998",
"field": "astro-ph.SR",
"target_year": 2025,
"num_input_papers": 3,
"input_total_words": 85000,
"target_total_words": 100000,
"target_page_count": 180,
"input_versions": ["v1", "v1", "v1"],
"target_version": "v1",
"input_licenses": ["...", "...", "..."],
"target_license": "..."
}
---
## Construction
Data is derived from arXiv.
Pipeline:
1. Scan arXiv submission comments for explicit terms like "PhD thesis" or "dissertation"
2. Enforce single-author constraint and 100-page threshold
3. Verify source availability (LaTeX or Word)
4. Identify self-authored publications within each thesis's bibliography (author surname match)
5. Map candidate chapter-forming papers to their arXiv records (extract explicit arXiv IDs from the citation strings → fallback title-match search)
6. Manually select chapter-forming papers from the candidates
---
## Splits
The dataset is split at the **thesis level**:
- Each target thesis appears in exactly one split
- All associated input papers remain within the same split
- No input paper identifier appears in more than one split
This design prevents document-level leakage.
---
## Statistics
Typical characteristics:
- Input papers per example: ~4.73
- Input length: ~60k words
- Thesis length: ~70k words
The scale exceeds the context window of most current models.
---
## Usage
This dataset is intended for:
- Long-context language modeling
- Multi-document synthesis
- Scientific writing generation
- Structure and discourse modeling at document scale
---
## Validation
A validation script is provided to ensure dataset integrity.
It checks:
- JSON format validity
- Required fields
- arXiv ID format
- Duplicate examples
- Duplicate target theses
- Input-paper overlap across splits
- Consistency of num_input_papers
- Presence of license fields
Run:
python scripts/validate_ids.py --data_dir data/
---
## Data Access
This dataset does **not** include full text of papers or theses.
The release contains only:
- arXiv identifiers
- metadata
- dataset splits
To retrieve PDFs:
python scripts/download_arxiv_pdfs.py --input data/test.jsonl --out_dir local_pdfs/
To retrieve LaTeX/source files:
python scripts/download_arxiv_sources.py --input data/test.jsonl --out_dir local_sources/
---
## Licensing
This dataset (metadata and scripts) is released under
Creative Commons Attribution 4.0 International (CC BY 4.0).
The dataset does not redistribute arXiv PDFs, source files, or extracted text.
All referenced arXiv documents remain subject to their original licenses, including:
- arXiv Non-Exclusive Distribution License
- Creative Commons licenses (e.g., CC BY, CC BY-SA)
Users are responsible for complying with the license terms of each document.
---
## Citation
Author and citation information have been redacted to preserve anonymity during peer review. A complete citation will be provided upon acceptance. |