Scientific article summarization is challenging: large, annotated corpora are
not available, and the summary should ideally include the article's impacts on
research community. This paper provides novel solutions to these two
challenges. We 1) develop and release the first large-scale manually-annotated
corpus for scientific papers (on computational linguistics) by enabling faster
annotation, and 2) propose summarization methods that integrate the authors'
original highlights (abstract) and the article's actual impacts on the
community (citations), to create comprehensive, hybrid summaries. We conduct
experiments to demonstrate the efficacy of our corpus in training data-driven
models for scientific paper summarization and the advantage of our hybrid
summaries over abstracts and traditional citation-based summaries. Our large
annotated corpus and hybrid methods provide a new framework for scientific
paper summarization research.

该论文提出了一种新的解决方案，通过开发和发布一个大规模的、手动注释的科学论文语料库，并提出了将作者的原始亮点（摘要）和文章对社区的实际影响（引用）整合的摘要方法，创建全面的、混合型摘要。我们进行了实验，证明了我们的语料库在训练数据驱动模型的科学论文摘要中的功效以及我们的混合型摘要优于摘要和传统的基于引用的摘要。这个大规模的注释语料库和混合方法提供了一个新的科学论文摘要研究框架。

ScisummNet: 带有引文网络的科学论文摘要的大型注释语料库和内容影响模型

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

Evaluation of text summarization approaches have been mostly based on metrics
that measure similarities of system generated summaries with a set of human
written gold-standard summaries. The most widely used metric in summarization
evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps
between the terms and phrases in the sentences; therefore, in cases of
terminology variations and paraphrasing, ROUGE is not as effective. Scientific
article summarization is one such case that is different from general domain
summarization (e.g. newswire data). We provide an extensive analysis of ROUGE's
effectiveness as an evaluation metric for scientific summarization; we show
that, contrary to the common belief, ROUGE is not much reliable in evaluating
scientific summaries. We furthermore show how different variants of ROUGE
result in very different correlations with the manual Pyramid scores. Finally,
we propose an alternative metric for summarization evaluation which is based on
the content relevance between a system generated summary and the corresponding
human written summaries. We call our metric SERA (Summarization Evaluation by
Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations
with manual scores which shows its effectiveness in evaluation of scientific
article summarization.

本篇论文专门研究了用于科技文章摘要的评估度量，对 ROUGE 的效果进行广泛分析，发现 ROUGE 不太可靠，提出了 SERA 作为科技文章摘要评估的一种有效指标。