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2020 | OriginalPaper | Chapter

A Compare-Aggregate Model with External Knowledge for Query-Focused Summarization

Authors: Jing Ya, Tingwen Liu, Li Guo

Published in: Web Information Systems Engineering – WISE 2020

Publisher: Springer International Publishing

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Abstract

Query-focused extractive summarization aims to create a summary by selecting sentences from original document according to query relevance and redundancy. With recent advances of neural network models in natural language processing, attention mechanism is widely used to address text summarization task. However, existing methods are always based on a coarse-grained sentence-level attention, which likely to miss the intent of query and cause relatedness misalignment. To address the above problem, we introduce a fine-grained and interactive word-by-word attention to the query-focused extractive summarization system. In that way, we capture the real intent of query. We utilize a Compare-Aggregate model to implement the idea, and simulate the interactively attentive reading and thinking of human behavior. We also leverage external conceptual knowledge to enrich the model and fill the expression gap between query and document. In order to evaluate our method, we conduct experiments on DUC 2005–2007 query-focused summarization benchmark datasets. Experimental results demonstrate that our proposed approach achieves better performance than state-of-the-art.

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Literature
1.
go back to reference Dang, H.T.: Overview of Duc 2005. In: Proceedings of DUC, pp. 1–12 (2005) Dang, H.T.: Overview of Duc 2005. In: Proceedings of DUC, pp. 1–12 (2005)
2.
go back to reference Wenpeng, Y., Yulong, P.: Optimizing sentence modeling and selection for document summarization. In Proceedings of IJCAI, pp. 1383–1389 (2015) Wenpeng, Y., Yulong, P.: Optimizing sentence modeling and selection for document summarization. In Proceedings of IJCAI, pp. 1383–1389 (2015)
3.
go back to reference Ziqiang, C., Furu, W., Sujian, L., Wenjie, L., Ming, Z., Houfeng, W.: Learning summary prior representation for extractive summarization. In: Proceedings of IJCAI, Short Paper, pp. 829–833 (2015) Ziqiang, C., Furu, W., Sujian, L., Wenjie, L., Ming, Z., Houfeng, W.: Learning summary prior representation for extractive summarization. In: Proceedings of IJCAI, Short Paper, pp. 829–833 (2015)
4.
go back to reference Ziqiang, C., Wenjie, L., Sujian, L., Furu, W., Yanran, L.:. AttSum: joint learning of focusing and summarization with neural attention. In: Proceedings of COLING, pp. 547–556 (2016) Ziqiang, C., Wenjie, L., Sujian, L., Furu, W., Yanran, L.:. AttSum: joint learning of focusing and summarization with neural attention. In: Proceedings of COLING, pp. 547–556 (2016)
5.
go back to reference Preksha, N., Khapra, M.M., Anirban, L., Ravindran, B.: Diversity driven attention model for query-based abstractive summarization. In: Proceedings of ACL, pp. 1063–1072 (2017) Preksha, N., Khapra, M.M., Anirban, L., Ravindran, B.: Diversity driven attention model for query-based abstractive summarization. In: Proceedings of ACL, pp. 1063–1072 (2017)
6.
go back to reference Shuohang, W., Jing, J.: A compare-aggregate model for matching text sequences. In: Proceedings of ICLR (2017) Shuohang, W., Jing, J.: A compare-aggregate model for matching text sequences. In: Proceedings of ICLR (2017)
7.
go back to reference Parikh Ankur, P., Oscar, T., Dipanjan, D., Jakob, U.: A decomposable attention model for natural language inference. In: Proceedings of EMNLP, pp. 2249–2255 (2016) Parikh Ankur, P., Oscar, T., Dipanjan, D., Jakob, U.: A decomposable attention model for natural language inference. In: Proceedings of EMNLP, pp. 2249–2255 (2016)
8.
go back to reference Weijie, B., Si, L., Zhao, Y., Guang, C., Zhiqing, L.: A compare-aggregate model with dynamic-clip attention for answer selection. In: Proceedings of CIKM, Short Paper, pages pp. 1987–1990 (2017) Weijie, B., Si, L., Zhao, Y., Guang, C., Zhiqing, L.: A compare-aggregate model with dynamic-clip attention for answer selection. In: Proceedings of CIKM, Short Paper, pages pp. 1987–1990 (2017)
9.
go back to reference Seunghyun, Y., Franck, D., Doo, K., Soon, B.T., Kyomin, J.: A compare-aggregate model with latent clustering for answer selection. In: Proceedings of CIKM, Short Paper, pp. 2093–2096 (2019) Seunghyun, Y., Franck, D., Doo, K., Soon, B.T., Kyomin, J.: A compare-aggregate model with latent clustering for answer selection. In: Proceedings of CIKM, Short Paper, pp. 2093–2096 (2019)
10.
go back to reference Arbi, B., Xiaohua, L., Jian-Yun, N.: Integrating multiple resources for diversified query expansion. In: Proceedings of ECIR, pp. 437–442 (2014) Arbi, B., Xiaohua, L., Jian-Yun, N.: Integrating multiple resources for diversified query expansion. In: Proceedings of ECIR, pp. 437–442 (2014)
11.
go back to reference Sarasi, L., Sujan, P., Pavan, K., Amit, S.: Domain-specific hierarchical subgraph extraction: a recommendation use case. In: Proceedings of Big Data, pp. 666–675 (2017) Sarasi, L., Sujan, P., Pavan, K., Amit, S.: Domain-specific hierarchical subgraph extraction: a recommendation use case. In: Proceedings of Big Data, pp. 666–675 (2017)
12.
go back to reference Sarasi, L., Sujan, P., Pavan, K., Amit, S.: Domain-specific hierarchical subgraph extraction: a recommendation use case. In: Proceedings of Big Data, pp. 666–675 (2017) Sarasi, L., Sujan, P., Pavan, K., Amit, S.: Domain-specific hierarchical subgraph extraction: a recommendation use case. In: Proceedings of Big Data, pp. 666–675 (2017)
13.
go back to reference Qian, C., Xiaodan, Z., Zhen-Hua, L., Diana, I., Si, W.: Neural natural language inference models enhanced with external knowledge. In: Proceedings of ACL, pp. 2406–2417 (2018) Qian, C., Xiaodan, Z., Zhen-Hua, L., Diana, I., Si, W.: Neural natural language inference models enhanced with external knowledge. In: Proceedings of ACL, pp. 2406–2417 (2018)
14.
go back to reference Robyn, S., Joshua, C., Catherine, H.: ConceptNet 5.5: an open multilingual graph of general knowledge. In: Proceedings of AAAI, pp. 4444–4451 (2017) Robyn, S., Joshua, C., Catherine, H.: ConceptNet 5.5: an open multilingual graph of general knowledge. In: Proceedings of AAAI, pp. 4444–4451 (2017)
15.
go back to reference Jacob, D., Ming-Wei, C., Kenton, L., Kristina, T.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171–4186 (2019) Jacob, D., Ming-Wei, C., Kenton, L., Kristina, T.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171–4186 (2019)
16.
go back to reference Ashish, V., et al.: Attention is all you need. In: Proceedings of NIPS, pp. 5998–6008 (2017) Ashish, V., et al.: Attention is all you need. In: Proceedings of NIPS, pp. 5998–6008 (2017)
17.
go back to reference Robyn, S., Joanna, L.-D.: ConceptNet at SemEval-2017 Task 2: extending word embeddings with multilingual relational knowledge. In: Proceedings of SemEval workshop at ACL 2017, pp. 85–89 (2017) Robyn, S., Joanna, L.-D.: ConceptNet at SemEval-2017 Task 2: extending word embeddings with multilingual relational knowledge. In: Proceedings of SemEval workshop at ACL 2017, pp. 85–89 (2017)
18.
go back to reference Yoon, K.: Convolutional neural networks for sentence classification. In: Proceedings of EMNLP, pp. 1746–1751 (2014) Yoon, K.: Convolutional neural networks for sentence classification. In: Proceedings of EMNLP, pp. 1746–1751 (2014)
19.
go back to reference Guy, F., Haggai, R., Odellia, B., David, K.: Unsupervised query-focused multi-document summarization using the cross entropy method. In: Proceedings of SIGIR, Short Paper, pp. 961–964 (2017) Guy, F., Haggai, R., Odellia, B., David, K.: Unsupervised query-focused multi-document summarization using the cross entropy method. In: Proceedings of SIGIR, Short Paper, pp. 961–964 (2017)
20.
go back to reference Jaime, C., Jade, G.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of SIGIR, Short Paper, pp. 335–336 (1998) Jaime, C., Jade, G.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of SIGIR, Short Paper, pp. 335–336 (1998)
21.
go back to reference Xiaojun, W., Jianguo, X.: Graph-based multi-modality learning for topic-focused multi-document summarization. In: Proceedings of IJCAI, pp. 1586–1591 (2009) Xiaojun, W., Jianguo, X.: Graph-based multi-modality learning for topic-focused multi-document summarization. In: Proceedings of IJCAI, pp. 1586–1591 (2009)
22.
go back to reference Sheng-hua, Z., Yan, L., Bin, L., Jing, L.: Query-oriented unsupervised multi-document summarization via deeplearning model. Expert Syst. Appl. 42(21), 8146–8155 (2015) CrossRef Sheng-hua, Z., Yan, L., Bin, L., Jing, L.: Query-oriented unsupervised multi-document summarization via deeplearning model. Expert Syst. Appl. 42(21), 8146–8155 (2015) CrossRef
23.
go back to reference Mittul, S., Arunav, M.: Long-span language models for query-focused unsupervised extractive text summarization. In: Proceedings of ECIR, pp. 657–664 (2018) Mittul, S., Arunav, M.: Long-span language models for query-focused unsupervised extractive text summarization. In: Proceedings of ECIR, pp. 657–664 (2018)
24.
go back to reference Michel, G.: A skip-chain conditional random field for ranking meeting utterances by importance. In: Proceedings of EMNLP, pp. 364–372 (2006) Michel, G.: A skip-chain conditional random field for ranking meeting utterances by importance. In: Proceedings of EMNLP, pp. 364–372 (2006)
25.
go back to reference You, O., Wenjie, L., Sujian, L., Qin, L.: Applying regression models to query-focused multidocument summarization. Inf. Process. Manage. 47(2), 227–237 (2011) CrossRef You, O., Wenjie, L., Sujian, L., Qin, L.: Applying regression models to query-focused multidocument summarization. Inf. Process. Manage. 47(2), 227–237 (2011) CrossRef
26.
go back to reference Chen, L., Xian, Q., Yang, L.: Using supervised bigram-based ILP for extractive summarization. In: Proceedings of ACL, pp. 1004–1013 (2013) Chen, L., Xian, Q., Yang, L.: Using supervised bigram-based ILP for extractive summarization. In: Proceedings of ACL, pp. 1004–1013 (2013)
27.
go back to reference Chao, S., Tao,L.: Learning to rank for query-focused multi-document summarization. In: Proceedings of ICDM, pp. 626–634 (2011) Chao, S., Tao,L.: Learning to rank for query-focused multi-document summarization. In: Proceedings of ICDM, pp. 626–634 (2011)
28.
go back to reference Jianpeng, C., Lapata, M.: Neural summarization by extracting sentences and words. In: Proceedings of ACL, pp. 484–494 (2016) Jianpeng, C., Lapata, M.: Neural summarization by extracting sentences and words. In: Proceedings of ACL, pp. 484–494 (2016)
29.
go back to reference Pengjie, R., Zhumin, C.: Sentence relation for extractive summarization with deep neural network. TOIS 36(4), 1–32 (2018) Pengjie, R., Zhumin, C.: Sentence relation for extractive summarization with deep neural network. TOIS 36(4), 1–32 (2018)
30.
go back to reference Kobayashi Hayato, M.N., Yatsuka, T.: Summarization based on embedding distributions. In: Proceedings of EMNLP, pp. 1984–1989 (2015) Kobayashi Hayato, M.N., Yatsuka, T.: Summarization based on embedding distributions. In: Proceedings of EMNLP, pp. 1984–1989 (2015)
31.
go back to reference Yanran, L., Li, S.: Query-focused multi-document summarization: combining a topic model with graph-based semi-supervised learning. In: Proceedings of COLING, pp. 1197–1207 (2014) Yanran, L., Li, S.: Query-focused multi-document summarization: combining a topic model with graph-based semi-supervised learning. In: Proceedings of COLING, pp. 1197–1207 (2014)
32.
go back to reference Tatsuya, I., Kazuya, M., Hayato, K., Hiroya, T., Manabu, O.: Distant supervision for extractive question summarization. In: Proceedings of ECIR, pp. 182–189 (2020) Tatsuya, I., Kazuya, M., Hayato, K., Hiroya, T., Manabu, O.: Distant supervision for extractive question summarization. In: Proceedings of ECIR, pp. 182–189 (2020)
Metadata
Title
A Compare-Aggregate Model with External Knowledge for Query-Focused Summarization
Authors
Jing Ya
Tingwen Liu
Li Guo
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-62008-0_5

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