An Empirical Assessment of Citation Information in Scientific Summarization

Abstract

Abstract. Considering the recent substantial growth of the publication rate of scientific results, nowadays the availability of effective and automated techniques to summarize scientific articles is of utmost importance. In this paper we investigate if and how we can exploit the citations of an article in order to better identify its relevant excerpts. By relying on the BioSumm2014 dataset, we evaluate the variation in performance of extractive summarization approaches when we consider the citations to extend or select the contents of an article to summarize. We compute the maximum ROUGE-2 scores that can be obtained when we summarize a paper by considering its contents together with its citations. We show that the inclusion of citation-related information brings to the generation of better summaries.

Automatic related work section generation: experiments in scientific document abstracting

“…Their approach identifies different aspects of the scientific paper extracting representative sentences for each aspect. Mohammad et al (2009) performed experiments to show the helpfulness of citation text to automatically generate technical surveys while (Ronzano and Saggion, 2016) using data from the BioSumm 2014 Challenge studied performance gains when using citation sentences to summarize a scientific article.…”

Generating Summaries for Scientific Paper Review

“…Top conferences in NLP have organized workshops on scholarly document processing, including shared tasks specifically focused on scientific document summarization (Chandrasekaran et al, 2019). Most approaches for scientific text summarization use an extractive (Saggion and Lapalme, 2000;Saggion, 2011;Yang et al, 2016;Slamet et al, 2018;Agrawal et al, 2019;Hoang and Kan, 2010) or citation-based approach (Cohan and Goharian, 2017;Qazvinian et al, 2010;Ronzano and Saggion, 2016), with a few exceptions attempting abstractive summarization on scientific texts (Lloret et al, 2013). Notably, Ju et al (2020) use a combined extractive and abstractive approach based on BERT.…”