Deep Keyphrase Generation

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 582–592 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1054 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 582–592 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1054 Deep Keyphrase Generation Rui Meng, Sanqiang Zhao, Shuguang Han, Daqing He∗, Peter Brusilovsky, Yu Chi School of Computing and Information University of Pittsburgh Pittsburgh, PA, 15213 {rui.meng, saz31, shh69, daqing, peterb, yuc73}@pitt.edu Abstract Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retriev- ing text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analy- sis on six datasets demonstrates that our proposed model not only achieves a sig- nificant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at https://github.com/memray/seq2seq- keyphrase. 1 Introduction A keyphrase or keyword is a piece of short, sum- mative content that expresses the main semantic meaning of a longer text. The typical use of a keyphrase or keyword is in scientific publications to provide the core information of a paper. We use ∗Corresponding author the term “keyphrase” interchangeably with “key- word” in the rest of this paper, as both terms have an implication that they may contain mul- tiple words. High-quality keyphrases can facili- tate the understanding, organizing, and accessing of document content. As a result, many studies have focused on ways of automatically extracting keyphrases from textual content (Liu et al., 2009; Medelyan et al., 2009a; Witten et al., 1999). Due to public accessibility, many scientific publication datasets are often used as test beds for keyphrase extraction algorithms. Therefore, this study also focuses on extracting keyphrases from scientific publications. Automatically extracting keyphrases from a document is called keypharase extraction, and it has been widely used in many applications, such as information retrieval (Jones and Staveley, 1999), text summarization (Zhang et al., 2004), text categorization (Hulth and Megyesi, 2006), and opinion mining (Berend, 2011). Most of the existing keyphrase extraction algorithms have addressed this problem through two steps (Liu et al., 2009; Tomokiyo and Hurst, 2003). The first step is to acquire a list of keyphrase candi- dates. Researchers have tried to use n-grams or noun phrases with certain part-of-speech patterns for identifying potential candidates (Hulth, 2003; Le et al., 2016; Liu et al., 2010; …

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