MS2: Multi-Document Summarization of Medical Studies

Jay DeYoung, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl, and Lucy Lu Wang
EMNLP  2021

Tl;DR: To assess the effectiveness of any medical intervention, researchers must conduct a time-intensive and highly manual literature review. NLP systems can help to automate or assist in parts of this expensive process. In support of this goal, we release MS^2 (Multi-Document Summarization of Medical Stu... dies), a dataset of over 470k documents and 20k summaries derived from the scientific literature. This dataset facilitates the development of systems that can assess and aggregate contradictory…

On Generating Extended Summaries of Long Documents

Sajad Sotudeh, Arman Cohan, and Nazli Goharian
preprint  2020

Tl;DR: We present a new hierarchical extractive method for generating extended summaries of long papers.

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TLDR: Extreme Summarization of Scientific Documents

Isabel Cachola, Kyle Lo, Arman Cohan, and Daniel S. Weld
EMNLP Findings  2020

Tl;DR: We introduce TLDR generation for scientific papers, a new automatic summarization task with high source compression and provide a new dataset and models for effective generation of TLDRs.

Citation Text Generation

Kelvin Luu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, and Noah A. Smith
preprint  2020

Tl;DR: We introduce the task of citation text generation: given a pair of scientific documents, explain their relationship in natural language text in the manner of a citation from one text to the other.

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