# SuMe: A Dataset Towards Summarizing Biomedical Mechanisms

arXiv: https://arxiv.org/abs/2205.04652 · HTML: https://mlsanity.com/paper/2205.04652/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 30 of 30)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [TLDR: Extreme Summarization of Scientific Documents](https://mlsanity.com/paper/2004.15011/) (2020)
- [SciFive: a text-to-text transformer model for biomedical literature](https://mlsanity.com/paper/2106.03598/) (2021)
- [Learning Compact Metrics for MT](https://mlsanity.com/paper/2110.06341/) (2021)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [DARE: Data Augmented Relation Extraction with GPT-2](https://mlsanity.com/paper/2004.13845/) (2020)
- [Evidence Inference 2.0: More Data, Better Models](https://mlsanity.com/paper/2005.04177/) (2020)
- [PaperRobot: Incremental Draft Generation of Scientific Ideas](https://mlsanity.com/paper/1905.07870/) (2019)
- [A Supervised Approach to Extractive Summarisation of Scientific Papers](https://mlsanity.com/paper/1706.03946/) (2017)
- [A Survey on Explainability in Machine Reading Comprehension](https://mlsanity.com/paper/2010.00389/) (2020)
- [Domain Adaptation with Adversarial Training and Graph Embeddings](https://mlsanity.com/paper/1805.05151/) (2018)
- [Saliency Learning: Teaching the Model Where to Pay Attention](https://mlsanity.com/paper/1902.08649/) (2019)
- [TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks](https://mlsanity.com/paper/1906.01351/) (2019)
- [Author's Sentiment Prediction](https://mlsanity.com/paper/2011.06128/) (2020)
- [MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning](https://mlsanity.com/paper/2004.10220/) (2020)
- [Two Huge Title and Keyword Generation Corpora of Research Articles](https://mlsanity.com/paper/2002.04689/) (2020)
- [Paper Abstract Writing through Editing Mechanism](https://mlsanity.com/paper/1805.06064/) (2018)
- [Relation Extraction with Explanation](https://mlsanity.com/paper/2005.14271/) (2020)
- [Modeling Label Semantics for Predicting Emotional Reactions](https://mlsanity.com/paper/2006.05489/) (2020)
- [Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach](https://mlsanity.com/paper/2109.14638/) (2021)

## Built on by (most cited first, 2 of 2)

- [BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples](https://mlsanity.com/paper/2210.14814/) (2022)
- [MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts](https://mlsanity.com/paper/2604.06505/) (2026)

## Abstract (arXiv)

Can language models read biomedical texts and explain the biomedical mechanisms discussed? In this work we introduce a biomedical mechanism summarization task. Biomedical studies often investigate the mechanisms behind how one entity (e.g., a protein or a chemical) affects another in a biological context. The abstracts of these publications often include a focused set of sentences that present relevant supporting statements regarding such relationships, associated experimental evidence, and a concluding sentence that summarizes the mechanism underlying the relationship. We leverage this structure and create a summarization task, where the input is a collection of sentences and the main entities in an abstract, and the output includes the relationship and a sentence that summarizes the mechanism. Using a small amount of manually labeled mechanism sentences, we train a mechanism sentence classifier to filter a large biomedical abstract collection and create a summarization dataset with 22k instances. We also introduce conclusion sentence generation as a pretraining task with 611k instances. We benchmark the performance of large bio-domain language models. We find that while the pretraining task help improves performance, the best model produces acceptable mechanism outputs in only 32% of the instances, which shows the task presents significant challenges in biomedical language understanding and summarization.