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Scientific article summarization is challenging: large, annotated corpora arenot available, and the summary should ideally include the article's impacts onresearch community. This paper provides novel solutions to these twochallenges. We 1) develop and release the first large-scale manually-annotatedcorpus for scientific papers (on computational linguistics) by enabling fasterannotation, and 2) propose summarization methods that integrate the authors'original highlights (abstract) and the article's actual impacts on thecommunity (citations), to create comprehensive, hybrid summaries. We conductexperiments to demonstrate the efficacy of our corpus in training data-drivenmodels for scientific paper summarization and the advantage of our hybridsummaries over abstracts and traditional citation-based summaries. Our largeannotated corpus and hybrid methods provide a new framework for scientificpaper summarization research.

ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks