At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization

Proceedings of the 28th International Conference on Computational Linguistics, pages 5617–5628 Barcelona, Spain (Online), December 8-13, 2020 5617 At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization Qingyu Zhou∗ Tencent Cloud Xiaowei Beijing, China qingyuzhou@tencent.com Furu Wei, Ming Zhou Microsoft Research Asia Beijing, China {fuwei,mingzhou}@microsoft.com Abstract Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence level. However, it is unclear whether performing extraction at sentence level is the best solution. In this work, we show that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising alternative. Specifically, we propose extracting sub-sentential units based on the constituency parsing tree. A neural extractive model which leverages the sub- sentential information and extracts them is presented. Extensive experiments and analyses show that extracting sub-sentential units performs competitively comparing to full sentence extraction under the evaluation of both automatic and human evaluations. Hopefully, our work could pro- vide some inspiration of the basic extraction units in extractive summarization for future research. 1 Introduction Automatic text summarization aims to produce a brief piece of text which can preserve the most im- portant information in it. The important contents are identified and then extracted to form the output summary (Nenkova and McKeown, 2011). In recent decades, extractive methods have proven effective in many systems (Carbonell and Goldstein, 1998; Mihalcea and Tarau, 2004; McDonald, 2007; Cao et al., 2015; Cheng and Lapata, 2016; Zhou et al., 2018; Nallapati et al., 2017). In previous works, extractive summarization systems perform extraction on the sentence level (Mihal- cea and Tarau, 2004; Cheng and Lapata, 2016; Nallapati et al., 2017). As the extraction unit, a sentence is a grammatical unit of one or more words that express a statement, question, request, etc. There are several advantages of extracting sentences. First, extractive systems are simpler, easier to develop, and faster during run-time in real application scenarios, compared with abstractive systems. Moreover, origi- nal sentences in the input are naturally fluent and grammatically correct. Finally, extracted sentences are factually faithful to the input document, compared with abstractive methods (Cao et al., 2018). Despite the success of extractive systems, from previous works, it is still not clear whether extracting at sentence level is the best solution for extractive methods. There are several drawbacks of extracting the full sentences. The most obvious issue is that the extracted sentences may contain unnecessary informa- tion. Some previous works have also noticed this problem and try to solve it by compressing or rewriting the extracted sentences (Martins and Smith, 2009; Chen and Bansal, 2018; Xu and Durrett, 2019). Fur- thermore, extracted sentences may contain duplicate contents. Thus, methods such as Maximal Marginal Relevance (MMR) (Carbonell and Goldstein, 1998) and sentence fusion (Barzilay and McKeown, 2005; Lebanoff et al., 2019) are proposed to avoid or merge duplicate contents. The redundancy and unnecessity issues might be caused by extracting full sentences since an important sentence may also …

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