# ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models

arXiv: https://arxiv.org/abs/2410.22360 · HTML: https://mlsanity.com/paper/2410.22360/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 57 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 29 of 29)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://mlsanity.com/paper/2105.03011/) (2021)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [Decontextualization: Making Sentences Stand-Alone](https://mlsanity.com/paper/2102.05169/) (2021)
- [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://mlsanity.com/paper/2010.14235/) (2020)
- [LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation](https://mlsanity.com/paper/2305.11116/) (2023)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020)
- [Text-to-Table: A New Way of Information Extraction](https://mlsanity.com/paper/2109.02707/) (2021)
- [ASPECTNEWS: Aspect-Oriented Summarization of News Documents](https://mlsanity.com/paper/2110.08296/) (2021)
- [Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking](https://mlsanity.com/paper/2308.07517/) (2023)
- [Schema-Driven Information Extraction from Heterogeneous Tables](https://mlsanity.com/paper/2305.14336/) (2023)
- [OASum: Large-Scale Open Domain Aspect-based Summarization](https://mlsanity.com/paper/2212.09233/) (2022)
- [unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network](https://mlsanity.com/paper/2303.14957/) (2023)
- [WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections](https://mlsanity.com/paper/2012.14919/) (2020)
- [Augmenting Scientific Creativity with an Analogical Search Engine](https://mlsanity.com/paper/2205.15476/) (2022)
- [PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers](https://mlsanity.com/paper/2403.02939/) (2024)
- [DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles](https://mlsanity.com/paper/2207.01079/) (2022)
- [A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents](https://mlsanity.com/paper/2305.14772/) (2023)
- [QualEval: Qualitative Evaluation for Model Improvement](https://mlsanity.com/paper/2311.02807/) (2023)
- [Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas](https://mlsanity.com/paper/2102.09761/) (2021)
- [Scientific Table Search Using Keyword Queries](https://mlsanity.com/paper/1707.03423/) (2017)
- [On-the-fly Table Generation](https://mlsanity.com/paper/1805.04875/) (2018)
- [CTE: A Dataset for Contextualized Table Extraction](https://mlsanity.com/paper/2302.01451/) (2023)

## Built on by (most cited first, 9 of 9)

- [From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery](https://mlsanity.com/paper/2508.14111/) (2025)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [Table-R1: Inference-Time Scaling for Table Reasoning](https://mlsanity.com/paper/2505.23621/) (2025)
- [AutoLabs: Cognitive Multi-Agent Systems with Self-Correction for Autonomous Chemical Experimentation](https://mlsanity.com/paper/2509.25651/) (2025)
- [A Survey of AI Scientists](https://mlsanity.com/paper/2510.23045/) (2025)
- [AI for Auto-Research: Roadmap & User Guide](https://mlsanity.com/paper/2605.18661/) (2026)
- [ScienceMeter: Tracking Scientific Knowledge Updates in Language Models](https://mlsanity.com/paper/2505.24302/) (2025)
- [Preface to the Special Issue of the TAL Journal on Scholarly Document Processing](https://mlsanity.com/paper/2506.03587/) (2025)
- [How Is Automated Research Evaluated? A Survey of Benchmarks and Evaluation Practices](https://mlsanity.com/paper/2610.11877/) (2026)

## Abstract (arXiv)

When conducting literature reviews, scientists often create literature review tables - tables whose rows are publications and whose columns constitute a schema, a set of aspects used to compare and contrast the papers. Can we automatically generate these tables using language models (LMs)? In this work, we introduce a framework that leverages LMs to perform this task by decomposing it into separate schema and value generation steps. To enable experimentation, we address two main challenges: First, we overcome a lack of high-quality datasets to benchmark table generation by curating and releasing arxivDIGESTables, a new dataset of 2,228 literature review tables extracted from ArXiv papers that synthesize a total of 7,542 research papers. Second, to support scalable evaluation of model generations against human-authored reference tables, we develop DecontextEval, an automatic evaluation method that aligns elements of tables with the same underlying aspects despite differing surface forms. Given these tools, we evaluate LMs' abilities to reconstruct reference tables, finding this task benefits from additional context to ground the generation (e.g. table captions, in-text references). Finally, through a human evaluation study we find that even when LMs fail to fully reconstruct a reference table, their generated novel aspects can still be useful.