Many scientific papers such as those in arXiv and PubMed data collections
have abstracts with varying lengths of 50-1000 words and average length of
approximately 200 words, where longer abstracts typically convey more
information about the source paper. Up to recently, scientific summarization
research has typically focused on generating short, abstract-like summaries
following the existing datasets used for scientific summarization. In domains
where the source text is relatively long-form, such as in scientific documents,
such summary is not able to go beyond the general and coarse overview and
provide salient information from the source document. The recent interest to
tackle this problem motivated curation of scientific datasets, arXiv-Long and
PubMed-Long, containing human-written summaries of 400-600 words, hence,
providing a venue for research in generating long/extended summaries. Extended
summaries facilitate a faster read while providing details beyond coarse
information. In this paper, we propose TSTR, an extractive summarizer that
utilizes the introductory information of documents as pointers to their salient
information. The evaluations on two existing large-scale extended summarization
datasets indicate statistically significant improvement in terms of Rouge and
average Rouge (F1) scores (except in one case) as compared to strong baselines
and state-of-the-art. Comprehensive human evaluations favor our generated
extended summaries in terms of cohesion and completeness.

本文提出了一种能够利用文献中引言信息作为指针提取关键信息的提取式文本摘要方法，该方法在两个大规模的科学文献长篇摘要数据集上获得了显著的 Rouge 分数和人类评估的整体完整性和连贯性相较于传统 baseline 和现有先进技术的提升。

TSTR: 太短难以代表，通过详细汇总进行简介导向的扩展摘要生成

TSTR: Too Short to Represent, Summarize with Details! Intro-Guided Extended Summary Generation

Prior work in document summarization has mainly focused on generating short
summaries of a document. While this type of summary helps get a high-level view
of a given document, it is desirable in some cases to know more detailed
information about its salient points that can't fit in a short summary. This is
typically the case for longer documents such as a research paper, legal
document, or a book. In this paper, we present a new method for generating
extended summaries of long papers. Our method exploits hierarchical structure
of the documents and incorporates it into an extractive summarization model
through a multi-task learning approach. We then present our results on three
long summarization datasets, arXiv-Long, PubMed-Long, and Longsumm. Our method
outperforms or matches the performance of strong baselines. Furthermore, we
perform a comprehensive analysis over the generated results, shedding insights
on future research for long-form summary generation task. Our analysis shows
that our multi-tasking approach can adjust extraction probability distribution
to the favor of summary-worthy sentences across diverse sections. Our datasets,
and codes are publicly available at
this https URL

本研究基于多任务学习方法，利用文件的层次结构生成长篇文献的扩展摘要，并在三个大型数据集上验证其优于其他强基准模型的性能，为长篇文献摘要生成任务的未来研究提供了深入的洞见。