TLDR : Extreme Summarization of Scientific Documents (2020)

Abstract: We introduce TLDR generation, a new form of extreme summarization, for scientific papers. TLDR generation involves high source compression and requires expert background knowledge and understanding of complex domain-specific language. To facilitate study on this task, we introduce SCITLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SCITLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden. We propose CATTS, a simple yet effective learning strategy for generating TLDRs that exploits titles as an auxiliary training signal. CATTS improves upon strong baselines under both automated metrics and human evaluations. Data and code are publicly available at https://github.com/allenai/scitldr .

## introduction we introduce tldr 1 generation for scientific papers. an alternative to abstracts, tldrs focus on the key aspects of the paper, such as its main contributions, eschewing nonessential background or methodological details. given the increasing pace of publication @xcite and resulting difficulty in keeping up with the literature, tldrs can enable readers to quickly discern a paper's key points and decide whether it's worth reading. the goal of existing work in summarization of scientific documents is to generate abstracts or provide complimentary summaries to abstracts. @xcite @xcite . in contrast, tldr 1 tldr is an acronym that stands for "too long; didn't read," which is often used in online informal discussion (e.g., twitter or reddit) about scientific papers. for visual clarity, we omit the semi-colon. ## conclusion we introduce tldr generation for scientific papers, and release scitldr, a multi-target dataset of tldr-paper pairs. we also present catts, a simple yet effective learning strategy for improving tldr generation that exploits auxiliary training signal from paper titles. we show that our approach improves over strong modeling baselines. existing methods for scientific document summarization often make use of properties unique to those papers, like sections, citation contexts or scientific discourse roles. future work can examine how best to incorporate these properties to improve tldr generation models. additionally, while our experiments are limited to abstract-only and aic input spaces, we provide the full text of the source papers to support research into using longer input contexts. furthermore, the multiple target summaries in scitldr reflect diverse perspectives and can be used to support summarization research into training and evaluation techniques previously unavailable with existing datasets. finally, the idea of a tldr can differ between academic disciplines, and we leave such exploration open for future work.