research-article

Multi-Source Pointer Network for Product Title Summarization

Authors:

Xiaobo WangAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 7 - 16

https://doi.org/10.1145/3269206.3271722

Published: 17 October 2018 Publication History

Abstract

In this paper, we study the product title summarization problem in E-commerce applications for display on mobile devices. Comparing with conventional sentence summarization, product title summarization has some extra and essential constraints. For example, factual errors or loss of the key information are intolerable for E-commerce applications. Therefore, we abstract two more constraints for product title summarization: (i) do not introduce irrelevant information; (ii) retain the key information (e.g., brand name and commodity name). To address these issues, we propose a novel multi-source pointer network by adding a new knowledge encoder for pointer network. The first constraint is handled by pointer mechanism. For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism. For evaluation, we build a large collection of real-world product titles along with human-written short titles. Experimental results demonstrate that our model significantly outperforms the other baselines. Finally, online deployment of our proposed model has yielded a significant business impact, as measured by the click-through rate.

References

[1]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of ICLR .

[2]

Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization . Association for Computational Linguistics, Michigan, 65--72.

[3]

Michele Banko, Vibhu O. Mittal, and Michael J. Witbrock. 2000. Headline Generation Based on Statistical Translation. In Proceedings of ACL . Association for Computational Linguistics, 318--325.

Digital Library

[4]

Abhijnan Chakraborty, Bhargavi Paranjape, Sourya Kakarla, and Niloy Ganguly. 2016. Stop Clickbait: Detecting and preventing clickbaits in online news media. In Proceedings of ASONAM . 9--16.

Digital Library

[5]

Jianpeng Cheng and Mirella Lapata. 2016. Neural Summarization by Extracting Sentences and Words. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Berlin, Germany, 484--494.

[6]

Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation. In Proceedings of EMNLP . Association for Computational Linguistics, Doha, Qatar, 1724--1734.

[7]

Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016. Abstractive Sentence Summarization with Attentive Recurrent Neural Networks. In Proceedings of NAACL. Association for Computational Linguistics, San Diego, California, 93--98.

[8]

Trevor Cohn and Mirella Lapata. 2008. Sentence Compression Beyond Word Deletion. In Proceedings of COLING . Manchester, UK, 137--144.

Digital Library

[9]

Trevor Cohn and Mirella Lapata. 2013. An Abstractive Approach to Sentence Compression. ACM Trans. Intell. Syst. Technol., Vol. 4, 3, Article 41 (July 2013), bibinfonumpages35 pages.

Digital Library

[10]

Bonnie Dorr, David Zajic, and Richard Schwartz. 2003. Hedge Trimmer: A Parse-and-Trim Approach to Headline Generation. In Proceedings of the HLT-NAACL 03 Text Summarization Workshop. 1--8.

Digital Library

[11]

John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. J. Mach. Learn. Res., Vol. 12 (July 2011), 2121--2159.

Digital Library

[12]

Mihail Eric and Christopher Manning. 2017. A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue. In Proceedings of EACL . Association for Computational Linguistics, Valencia, Spain, 468--473.

[13]

Katja Filippova, Enrique Alfonseca, Carlos A. Colmenares, Lukasz Kaiser, and Oriol Vinyals. 2015. Sentence Compression by Deletion with LSTMs. In Proceedings of EMNLP . Association for Computational Linguistics, Lisbon, Portugal, 360--368.

[14]

Katja Filippova and Michael Strube. 2008. Dependency Tree Based Sentence Compression. In Proceedings of INLG . Association for Computational Linguistics, Salt Fork, Ohio, 25--32.

Digital Library

[15]

Dimitrios Galanis and Ion Androutsopoulos. 2010. An extractive supervised two-stage method for sentence compression. In Proceedings of NAACL . Association for Computational Linguistics, Los Angeles, California, 885--893.

Digital Library

[16]

Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep Sparse Rectifier Neural Networks. In Proceedings of AISTAT. PMLR, Fort Lauderdale, FL, USA, 315--323.

[17]

Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016. Incorporating Copying Mechanism in Sequence-to-Sequence Learning. In Proceedings of ACL. Association for Computational Linguistics, Berlin, Germany, 1631--1640.

[18]

Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016. Pointing the Unknown Words. In Proceedings of ACL . Association for Computational Linguistics, Berlin, Germany, 140--149.

[19]

Shizhu He, Cao Liu, Kang Liu, and Jun Zhao. 2017. Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning. In Proceedings of ACL . Association for Computational Linguistics, Vancouver, Canada, 199--208.

[20]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Comput., Vol. 9, 8 (Nov. 1997), 1735--1780.

Digital Library

[21]

Hongyan Jing. 2002. Using Hidden Markov Modeling to Decompose Human-written Summaries. Comput. Linguist., Vol. 28, 4 (Dec. 2002), 527--543.

Digital Library

[22]

Rudolf Kadlec, Martin Schmid, Ondvrej Bajgar, and Jan Kleindienst. 2016. Text Understanding with the Attention Sum Reader Network. In Proceedings of ACL . Association for Computational Linguistics, Berlin, Germany, 908--918.

[23]

Kevin Knight and Daniel Marcu. 2000. Statistics-Based Summarization - Step One: Sentence Compression. In Proceedings of AAAI . 703--710.

Digital Library

[24]

Chin-Yew Lin. 2004. ROUGE: A Package for Automatic Evaluation of Summaries. In Text Summarization Branches Out: Proceedings of the ACL-04 Workshop. Association for Computational Linguistics, Barcelona, Spain, 74--81.

[25]

Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomávs Kovciský, Fumin Wang, and Andrew Senior. 2016. Latent Predictor Networks for Code Generation. In Proceedings of ACL . Association for Computational Linguistics, Berlin, Germany, 599--609.

[26]

Ryan McDonald. 2006. Discriminative Sentence Compression with Soft Syntactic Evidence. In Proceedings of EACL . 297--304.

[27]

Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2017. Pointer Sentinel Mixture Models. In Proceedings of ICLR .

[28]

Yishu Miao and Phil Blunsom. 2016. Language as a Latent Variable: Discrete Generative Models for Sentence Compression. In Proceedings of EMNLP. Association for Computational Linguistics, Austin, Texas, 319--328.

[29]

Rada Mihalcea and Paul Tarau. 2004. TextRank: Bringing Order into Texts. In Proceedings of EMNLP 2004 . Association for Computational Linguistics, Barcelona, Spain, 404--411.

[30]

Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, and Zhi Jin. 2016. Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation. In Proceedings of COLING. Osaka, Japan, 3349--3358.

[31]

Ramesh Nallapati, Bowen Zhou, C'i cero Nogueira dos Santos, cC aglar Gü lcc ehre, and Bing Xiang. 2016. Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. In Proceedings of CoNLL. Berlin, Germany, 280--290.

[32]

Courtney Napoles, Chris Callison-Burch, Juri Ganitkevitch, and Benjamin Van Durme. 2011. Paraphrastic Sentence Compression with a Character-based Metric: Tightening without Deletion. In Proceedings of the Workshop on Monolingual Text-To-Text Generation. Association for Computational Linguistics, Portland, Oregon, 84--90.

Digital Library

[33]

Paul Over, Hoa Dang, and Donna Harman. 2007. DUC in Context. Inf. Process. Manage., Vol. 43, 6 (Nov. 2007), 1506--1520.

Digital Library

[34]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a Method for Automatic Evaluation of Machine Translation. In Proceedings of ACL . Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, 311--318.

Digital Library

[35]

Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013. On the difficulty of training recurrent neural networks. In Proceedings of ICML . PMLR, Atlanta, Georgia, USA, 1310--1318.

Digital Library

[36]

Romain Paulus, Caiming Xiong, and Richard Socher. 2018. A Deep Reinforced Model for Abstractive Summarization. In Proceedings of ICLR .

[37]

Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015. A Neural Attention Model for Abstractive Sentence Summarization. In Proceedings of EMNLP . Association for Computational Linguistics, Lisbon, Portugal, 379--389.

[38]

Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get To The Point: Summarization with Pointer-Generator Networks. In Proceedings of ACL. Association for Computational Linguistics, Vancouver, Canada, 1073--1083.

[39]

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Proceedings of NIPS . Curran Associates, Inc., 3104--3112.

Digital Library

[40]

Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2017. Abstractive Document Summarization with a Graph-Based Attentional Neural Model. In Proceedings of ACL . Association for Computational Linguistics, Vancouver, Canada, 1171--1181.

[41]

Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer Networks. In Proceedings of NIPS, C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett (Eds.). Curran Associates, Inc., 2692--2700.

Digital Library

[42]

Jingang Wang, Junfeng Tian, Long Qiu, Sheng Li, Jun Lang, Luo Si, and Man Lan. 2018. A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data. In Proceedings of AAAI .

[43]

Shuohang Wang and Jing Jiang. 2017. Machine Comprehension Using Match-LSTM and Answer Pointer. In Proceedings of ICLR .

[44]

Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017. Gated Self-Matching Networks for Reading Comprehension and Question Answering. In Proceedings of ACL . Association for Computational Linguistics, Vancouver, Canada, 189--198.

[45]

Kristian Woodsend, Yansong Feng, and Mirella Lapata. 2010. Title Generation with Quasi-Synchronous Grammar. In Proceedings of EMNLP . Association for Computational Linguistics, Cambridge, MA, 513--523.

Digital Library

[46]

Sander Wubben, Antal van den Bosch, and Emiel Krahmer. 2012. Sentence Simplification by Monolingual Machine Translation. In Proceedings of ACL . Association for Computational Linguistics, Jeju Island, Korea, 1015--1024.

Digital Library

[47]

Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma. 2017. Topic Aware Neural Response Generation. In Proceedings of AAAI. 3351--3357.

[48]

Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, and Rui Yan. 2017. Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems. In Proceedings of EMNLP. Association for Computational Linguistics, Copenhagen, Denmark, 2190--2199.

[49]

David M. Zajic, Bonnie J. Dorr, and Richard M. Schwartz. 2004. BBN/UMD at DUC-2004: Topiary. In Proceedings of the HLT-NAACL 2004 Document Understanding Workshop. 112----119.

Cited By

Apaydın ASöylemez EGüneş MGürel Söylemez TKoç Apaydın Z(2024)CERVICAL PROPRIOCEPTION AND VESTIBULAR FUNCTIONS IN PATIENTS WITH NECK PAIN AND CERVICOGENIC HEADACHE: A COMPARATIVE STUDYJournal of Turkish Spinal Surgery10.4274/jtss.galenos.2024.75047(113-118)Online publication date: 8-Aug-2024
https://doi.org/10.4274/jtss.galenos.2024.75047
Deng JShi KHuo HWang DXu GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Homogeneous-listing-augmented Self-supervised Multimodal Product Title RefinementProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661347(2870-2874)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661347
Shakil HFarooq AKalita J(2024)Abstractive text summarization: State of the art, challenges, and improvementsNeurocomputing10.1016/j.neucom.2024.128255603(128255)Online publication date: Oct-2024
https://doi.org/10.1016/j.neucom.2024.128255
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Index Terms

Multi-Source Pointer Network for Product Title Summarization
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Summarization

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

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Published: 17 October 2018

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October 22 - 26, 2018

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Apaydın ASöylemez EGüneş MGürel Söylemez TKoç Apaydın Z(2024)CERVICAL PROPRIOCEPTION AND VESTIBULAR FUNCTIONS IN PATIENTS WITH NECK PAIN AND CERVICOGENIC HEADACHE: A COMPARATIVE STUDYJournal of Turkish Spinal Surgery10.4274/jtss.galenos.2024.75047(113-118)Online publication date: 8-Aug-2024
https://doi.org/10.4274/jtss.galenos.2024.75047
Deng JShi KHuo HWang DXu GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Homogeneous-listing-augmented Self-supervised Multimodal Product Title RefinementProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661347(2870-2874)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661347
Shakil HFarooq AKalita J(2024)Abstractive text summarization: State of the art, challenges, and improvementsNeurocomputing10.1016/j.neucom.2024.128255603(128255)Online publication date: Oct-2024
https://doi.org/10.1016/j.neucom.2024.128255
Quin FWeyns DGalster MSilva C(2024)A/B testingJournal of Systems and Software10.1016/j.jss.2024.112011211:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112011
Liu SYe ZLiao JWu JLi Z(2023)Unsupervised Product Title Optimization Based on Search Behavior Knowledge in E-commerce2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10192008(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10192008
Fukumoto KTakeuchi RNadamoto A(2022)Method for Evaluating Quality of Automatically Generated Product DescriptionsProceedings of the 11th International Symposium on Information and Communication Technology10.1145/3568562.3568583(52-58)Online publication date: 1-Dec-2022
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Zhao MYang YLi MWang JWu WRen Pde Rijke MRen ZAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Personalized Abstractive Opinion TaggingProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532037(1066-1076)Online publication date: 6-Jul-2022
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Guan XLong SZhu WCao SLiao F(2022)Mask-based Text Scoring for Product Title Summarization2022 8th International Conference on Systems and Informatics (ICSAI)10.1109/ICSAI57119.2022.10005399(1-6)Online publication date: 10-Dec-2022
https://doi.org/10.1109/ICSAI57119.2022.10005399
Wang J(2022)Research on Text Simplification Method Based on BERT2022 7th International Conference on Multimedia Communication Technologies (ICMCT)10.1109/ICMCT57031.2022.00023(78-81)Online publication date: Jul-2022
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Kedia SMantha AGupta SGuo SAchan K(2021)Generating Rich Product Descriptions for Conversational E-commerce SystemsCompanion Proceedings of the Web Conference 202110.1145/3442442.3451893(349-356)Online publication date: 19-Apr-2021
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