Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1027–1038 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 1027 Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, Fei Liu Computer Science Department University of Central Florida, Orlando, FL 32816, USA {swcho,loganlebanoff}@knight.ucf.edu, {foroosh,feiliu}@cs.ucf.edu Abstract The most important obstacles facing multi- document summarization include excessive re- dundancy in source descriptions and the loom- ing shortage of training data. These obstacles prevent encoder-decoder models from being used directly, but optimization-based methods such as determinantal point processes (DPPs) are known to handle them well. In this paper we seek to strengthen a DPP-based method for extractive multi-document summarization by presenting a novel similarity measure inspired by capsule networks. The approach measures redundancy between a pair of sentences based on surface form and semantic information. We show that our DPP system with improved sim- ilarity measure performs competitively, out- performing strong summarization baselines on benchmark datasets. Our findings are particu- larly meaningful for summarizing documents created by multiple authors containing redun- dant yet lexically diverse expressions.1 1 Introduction Multi-document summarization is arguably one of the most important tools for information aggrega- tion. It seeks to produce a succinct summary from a collection of textual documents created by mul- tiple authors concerning a single topic (Nenkova and McKeown, 2011). The summarization tech- nique has seen growing interest in a broad spec- trum of domains that include summarizing prod- uct reviews (Gerani et al., 2014; Yang et al., 2018), student survey responses (Luo and Litman, 2015; Luo et al., 2016), forum discussion threads (Ding and Jiang, 2015; Tarnpradab et al., 2017), and news articles about a particular event (Hong et al., 2014). Despite the empirical success, most of the datasets remain small, and the cost of hiring hu- 1Our code and data are publicly available at https://github. com/ucfnlp/summarization-dpp-capsnet man annotators to create ground-truth summaries for multi-document inputs can be prohibitive. Impressive progress has been made on neural abstractive summarization using encoder-decoder models (Rush et al., 2015; See et al., 2017; Paulus et al., 2017; Chen and Bansal, 2018). These mod- els, nonetheless, are data-hungry and learn poorly from small datasets, as is often the case with multi- document summarization. To date, studies have primarily focused on single-document summariza- tion (See et al., 2017; Celikyilmaz et al., 2018; Kryscinski et al., 2018) and sentence summariza- tion (Nallapati et al., 2016; Zhou et al., 2017; Cao et al., 2018; Song et al., 2018) in part because par- allel training data are abundant and they can be conveniently acquired from the Web. Further, a notable issue with abstractive summarization is the reliability. These models are equipped with the ca- pability of generating new words not present in the source. With greater freedom of lexical choices, the system summaries can contain inaccurate fac- tual details and falsified content that prevent them from staying “true-to-original.” In this paper we instead focus on an …

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