Citance-Contextualized Summarization of Scientific Papers (2023)

Abstract: Current approaches to automatic summarization of scientific papers generate informative summaries in the form of abstracts. However, abstracts are not intended to show the relationship between a paper and the references cited in it. We propose a new contextualized summarization approach that can generate an informative summary conditioned on a given sentence containing the citation of a reference (a so-called “citance”). This summary outlines content of the cited paper relevant to the citation location. Thus, our approach extracts and models the citances of a paper, retrieves relevant passages from cited papers, and generates abstractive summaries tailored to each citance. We evaluate our approach using **Webis-Context-SciSumm-2023**, a new dataset containing 540K computer science papers and 4.6M citances therein.

## introduction the original task of automatic summarization has been the abstracting of scientific papers, one of the first tasks studied in computer science @xcite . automatically generated abstracts were used to create "index volumes" for specific scientific areas to help researchers access the growing number of publications. nowadays, paper authors usually write abstracts themselves. however, author-generated abstracts often provide incomplete or biased coverage of scientific papers @xcite . as a result, the purpose of automatic paper summarization has evolved to generate more informative summaries, often using abstractive summarization approaches @xcite @xcite . a practical application of generating summaries is to augment reading papers. for example, cite-read by @xcite is part of allen ai's semantic reader @xcite and shows on demand the abstracts as summaries for the cited papers in a paper being read. while these [...] additionally, nematus supports minimum risk training (mrt) @xcite to optimize towards an arbitrary, sentence-level loss function. various mt metrics are supported as loss functions, including smoothed sentence-level bleu (chen and cherry, 2014), @xcite , beer @xcite , and any interpolation of implemented metrics. to stabilize training, nematus supports early stopping based on cross entropy or an arbitrary loss function defined by the user. [...] ## related work in this section, we review the literature on generic and citation-based summarization of scientific documents, including different types of generated summaries, and approaches that summarize the target paper-based on its citance (contexts), respectively. ## webis-context-scisumm-2023: a large-scale corpus for contextualized summarization of scientific papers previous datasets for summarizing scientific papers do not consider different types of citance contexts, nor do they evaluate multiple retrieval models for extracting relevant content (section 2). therefore, these datasets are not suitable for studying citancecontextualized summarization. to address this gap, we introduce webis-context-scisumm-2023, a new and large dataset created using our approach as described in section 3. ## evaluation we evaluated the retrieval step of our approach in an internal evaluation, and the summarization step in an external one. ## conclusion we investigated the use of generic paper abstracts compared to tailored contextualized summaries to improve a reader's understanding of the relevance of individual paper citations. for this purpose, we developed a new summarization approach to generate citance-contextualized summaries. with webis-context-scisumm-2023, we compiled a large-scale corpus to facilitate research in this direction. experiments with zero-shot summarization using llms showed that abstracts are slightly preferred over contextualized summaries in terms of coverage and focus, while summaries generated using our approach are preferred when citances do not refer to the main contribution of a paper.