Automatic Dialogue Summary Generation for Customer Service

ABSTRACT

Dialogue summarization extracts useful information from a dialogue. It helps people quickly capture the highlights of a dialogue without going through long and sometimes twisted utterances. For customer service, it saves human resources currently required to write dialogue summaries. A main challenge of dialogue summarization is to design a mechanism to ensure the logic, integrity, and correctness of the summaries. In this paper, we introduce auxiliary key point sequences to solve this problem. A key point sequence describes the logic of the summary. In our training procedure, a key point sequence acts as an auxiliary label. It helps the model learn the logic of the summary. In the prediction procedure, our model predicts the key point sequence first and then uses it to guide the prediction of the summary. Along with the auxiliary key point sequence, we propose a novel Leader-Writer network. The Leader net predicts the key point sequence, and the Writer net predicts the summary based on the decoded key point sequence. The Leader net ensures the summary is logical and integral. The Writer net focuses on generating fluent sentences. We test our model on customer service scenarios. The results show that our model outperforms other models not only on BLEU and ROUGE-L score but also on logic and integrity.

References

1. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016).
2. Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2016. An actor-critic algorithm for sequence prediction. arXiv preprint arXiv:1607.07086 (2016).
3. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).
4. Siddhartha Banerjee, Prasenjit Mitra, and Kazunari Sugiyama. 2015. Multi-document abstractive summarization using ilp based multi-sentence compression.. In Proc. IJCAI. 1208--1214.
5. Michele Banko, Vibhu O Mittal, and Michael J Witbrock. 2000. Headline generation based on statistical translation. In Proc. ACL . 318--325.
6. Taylor Berg-Kirkpatrick, Dan Gillick, and Dan Klein. 2011. Jointly learning to extract and compress. In Proc. ACL. 481--490.
7. Yen-Chun Chen and Mohit Bansal. 2018. Fast abstractive summarization with reinforce-selected sentence rewriting. arXiv preprint arXiv:1805.11080 (2018).
8. Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. Proc. EMNLP (2014).
9. Sumit Chopra, Michael Auli, and Alexander M Rush. 2016. Abstractive sentence summarization with attentive recurrent neural networks. In Proc. NAACL . 93--98.
10. Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A discourse-aware attention model for abstractive summarization of long documents. In Proc. NAACL-HLT, Vol. 2. 615--621.
11. Katja Filippova, Enrique Alfonseca, Carlos A Colmenares, Lukasz Kaiser, and Oriol Vinyals. 2015. Sentence compression by deletion with lstms. In Proc. EMNLP. 360--368.
12. Chih-Wen Goo and Yun-Nung Chen. 2018. Abstractive dialogue summarization with sentence-gated modeling optimized by dialogue acts. arXiv preprint arXiv:1809.05715 (2018).
13. Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016. Incorporating copying mechanism in sequence-to-sequence learning. In Proc. ACL . 1631--1640.
14. Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016. Pointing the unknown words. In Proc. ACL. 140--149.
15. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proc. CVPR. 770--778.
16. Dan Hendrycks and Kevin Gimpel. 2016. Bridging nonlinearities and stochastic regularizers with gaussian error linear units. arXiv preprint 1606.08415 (2016).
17. Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, and Ming Zhou. 2017. Reinforced mnemonic reader for machine reading comprehension. arXiv preprint arXiv:1705.02798 (2017).
18. Hongyan Jing and Kathleen R McKeown. 2000. Cut and paste based text summarization. In Proc. NAACL. 178--185.
19. Diederik P Kingma and Jimmy Ba. 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
20. Kevin Knight and Daniel Marcu. 2000. Statistics-based summarization-step one: sentence compression. AAAI/IAAI , Vol. 2000 (2000), 703--710.
21. Chin-Yew Lin. 2004. Rouge: a package for automatic evaluation of summaries. Text Summarization Branches Out (2004).
22. Yishu Miao and Phil Blunsom. 2016. Language as a latent variable: discrete generative models for sentence compression. (2016), 319--328.
23. Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016. Abstractive text summarization using sequence-to-sequence rnns and beyond. (2016), 280--290.
24. Tatsuro Oya, Yashar Mehdad, Giuseppe Carenini, and Raymond Ng. 2014. A template-based abstractive meeting summarization: leveraging summary and source text relationships. In Proc. INLG. 45--53.
25. Haojie Pan, Junpei Zhou, Zhou Zhao, Yan Liu, Deng Cai, and Min Yang. 2018. Dial2desc: end-to-end dialogue description generation. arXiv preprint arXiv:1811.00185 (2018).
26. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proc. ACL . 311--318.
27. Romain Paulus, Caiming Xiong, and Richard Socher. 2017. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304 (2017).
28. Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015. Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732 (2015).
29. Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, and Vaibhava Goel. 2017. Self-critical sequence training for image captioning. In Proc. CVPR. 3.
30. Alexander M Rush, Sumit Chopra, and Jason Weston. 2015. A neural attention model for abstractive sentence summarization. (2015), 379--389.
31. Abigail See, Peter J Liu, and Christopher D Manning. 2017. Get to the point: summarization with pointer-generator networks. In Proc. ACL . 1073--1083.
32. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research , Vol. 15, 1 (2014), 1929--1958.
33. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proc. NeurIPS. 5998--6008.
34. Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. In Proc. NeurIPS. 2692--2700.
35. Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017. Gated self-matching networks for reading comprehension and question answering. In Proc. ACL. 189--198.
36. Ronald J Williams and David Zipser. 1989. A learning algorithm for continually running fully recurrent neural networks. Neural computation , Vol. 1, 2 (1989), 270--280.
37. Shasha Xie, Yang Liu, and Hui Lin. 2008. Evaluating the effectiveness of features and sampling in extractive meeting summarization. In Proc. SLT. 157--160.
38. Wenyuan Zeng, Wenjie Luo, Sanja Fidler, and Raquel Urtasun. 2016. Efficient summarization with read-again and copy mechanism. arXiv preprint arXiv:1611.03382 (2016).

Index Terms

• Information systems

  • Information systems applications

    • Data mining

    • Enterprise information systems

      • Enterprise applications