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Syst."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>\n            Text summarization on non-factoid question answering (NQA) aims at identifying the core information of redundant answer guidance using questions, which can dramatically improve answer readability and comprehensibility. Most existing approaches focus on extracting query-related sentences to construct a summary, where the logical connection of natural language and the hierarchical interpretable semantic association are often neglected, thus degrading performance. To address these issues, we propose a novel question-driven abstractive answer summarization model, called the\n            <jats:bold>H<\/jats:bold>\n            ierarchical\n            <jats:bold>S<\/jats:bold>\n            liding\n            <jats:bold>I<\/jats:bold>\n            nference\n            <jats:bold>G<\/jats:bold>\n            enerator (HSIG), to form inferable and interpretable summaries by explicitly introducing hierarchical information reasoning between questions and corresponding answers. Specifically, we first apply an elaborately designed hierarchical sliding fusion inference model to determine the most relevant question sentence-level representation that provides a deeper interpretable basis for sentence selection in summarization, which further increases computational performance on the premise of following the semantic inheritance structure. Additionally, to improve summary fluency, we construct a double-driven selective generator to integrate various semantic information from two mutual question-and-answer perspectives. Experimental results illustrate that compared with state-of-the-art baselines, our model achieves remarkable improvement on two benchmark datasets and specifically improves the 2.46 ROUGE-1 points on PubMedQA, which demonstrates the superiority of our model on abstractive summarization with hierarchical sequential reasoning.\n          <\/jats:p>","DOI":"10.1145\/3511891","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T20:19:19Z","timestamp":1644869959000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Hierarchical Sliding Inference Generator for Question-driven Abstractive Answer Summarization"],"prefix":"10.1145","volume":"41","author":[{"given":"Bing","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Computer Network and Information Integration (Southeast University),Ministry of Education, China and School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China and School of Computer Science and Engineering, School of Cyber Science and Engineering, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanlin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computer Networkand Information Integration (Southeast University), Ministry of Education, China and School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Penghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computer Networkand Information Integration (Southeast University), Ministry of Education, China and School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijian","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computer Networkand Information Integration (Southeast University), Ministry of Education, China and School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICWR.2018.8387248"},{"key":"e_1_3_1_3_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations.","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. 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