QUERY-BASED SUMMARIZATION METHODS FOR CONVERSATIONAL AGENTS: AN OVERVIEW

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Published: Oct 20, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i8.4788
Keywords:
query based summarization, extractive summarization, abstractive summarization, Conversational agents, raw unstructured text data, text cubes,

Main Article Content

Ketakee Nimavat
UG student , Computer Engineering, L.D. College Of Engineering
Hetal Joshiara
Professor, Computer Engineering Department, L.D. College Of Engineering

Abstract

Summarization is a topic that will be of a great important in the coming age since intelligent assistants especially the ones in the form of conversational agents will have to sift through the abundance of raw unstructured text data to provide relevant information. The data will be in the form of Social media posts, content websites and other user generated text content from which the user shall require tailored information from and about the data. The paper hence explores various methods for summarization and focuses particularly on extracting the gist from the perspective of a given keyword i.e. query based summarization from raw unstructured text data sources available at scale. Along with that, the need for a proper framework to mine relevant knowledge from the said data is acknowledged and the challenges that a conversational agent would hence face are identified. Various approaches that contribute to building a framework and solve the identified challenges are explored as well. It is hoped that the approaches discussed in the paper will be of use to researchers building algorithms in areas of knowledge mining and understanding, such as summarization, that deal with the challenges that are expected to arise.

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Vol. 8 No. 8 (2017): September-October
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References

M. Gambhir and V. Gupta, “Recent automatic text summarization techniques: a survey,†Artif. Intell. Rev., vol. 47, no. 1, 2017.

A. Nenkova, “Automatic Summarization,†Found. Trends® Inf. Retr., vol. 5, no. 2, pp. 103–233, 2011.

S. Fisher and B. Roark, “Query-Focused Summarization By Supervised Sentence Ranking and Skewed Word Distributions,†Proc. 6th Doc. Underst. Conf. . DUC, 2006.

K.-F. Wong, M. Wu, and W. Li, “Extractive Summarization Using Supervised and Semi-supervised Learning,†Proc. 22nd Int. Conf. Comput. Linguist. 1. Assoc. Comput. Linguist. 2008., no. August, pp. 985–992, 2008.

L. Logeswaran, H. Lee, and D. Radev, “Sentence Ordering using Recurrent Neural Networks,†pp. 1–15, 2016.

S. Park and B. R. Cha, “Query-based multi-document summarization using non-negative semantic feature and NMF clustering,†Proc. - 4th Int. Conf. Networked Comput. Adv. Inf. Manag. NCM 2008, vol. 2, pp. 609–614, 2008.

V. Gupta, “Hybrid Algorithm for Multilingual Summarization,†pp. 717–727, 2013.

P. Nema, M. Khapra, A. Laha, and B. Ravindran, “Diversity driven Attention Model for Query-based Abstractive Summarization,†2017.

V. Gupta and G. S. Lehal, “A Survey of Text Summarization Extractive techniques,†J. Emerg. Technol. Web Intell., vol. 2, no. 3, pp. 258–268, 2010.

M. Allahyari et al., “Text Summarization Techniques: A Brief Survey,†no. 1, 2017.

J.-G. Yao, X. Wan, and J. Xiao, “Phrase-based Compressive Cross-Language Summarization,†Conf. Empir. Methods Nat. Lang. Process., no. September, pp. 118–127, 2015.

D. Wang, S. Zhu, and T. Li, “SumView: A Web-based engine for summarizing product reviews and customer opinions,†Expert Syst. Appl., vol. 40, no. 1, pp. 27–33, 2013.

D. Radev, W. Fan, H. Qi, H. Wu, and A. Grewal, “Probabilistic question answering on the Web,†J. Am. Soc. Inf. Sci. Technol., vol. 56, no. 6, pp. 571–583, 2005.

G. Carenini, R. T. Ng, and X. Zhou, “Summarizing email conversations with clue words,†Proc. 16th Int. Conf. World Wide Web - WWW ’07, p. 91, 2007.

A. Nenkova and A. Bagga, “Facilitating Email Thread Access by Extractive Summary Generation,†Recent Adv. Nat. Lang. Process. III, Sel. Pap. from RANLP’03, vol. 260, pp. 287–296, 2003.

O. Rambow, L. Shrestha, J. Chen, and C. Lauridsen, “Summarizing Email Threads,†Proc. HLT-NAACL 2004 Short Pap. XX - HLT-NAACL ’04, pp. 105–108, 2004.

M. A. H. Khan, D. Bollegala, G. Liu, and K. Sezaki, “Multi-tweet summarization of real-time events,†Proc. - Soc. 2013, no. September, pp. 128–133, 2013.

L. Shou, Z. Wang, K. Chen, and G. Chen, “Sumblr: continuous summarization of evolving tweet streams,†Proc. 36th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR ’13, p. 533, 2013.

X. Liu, Y. Li, F. Wei, and M. Zhou, “Graph-Based Multi-Tweet Summarization using Social Signals.,†Coling, vol. 2, no. December 2012, pp. 1699–1714, 2012.

T. Mondal, P. Pramanik, I. Bhattacharya, A. Saha, and N. Boral, “Towards development of FOPL based tweet summarization technique in a post disaster scenario: From survey to solution,†2017 51st Annu. Conf. Inf. Sci. Syst. CISS 2017, 2017.

J. B. S. Ong, Z. Wang, R. S. M. Goh, X. F. Yin, X. Xin, and X. Fu, “Understanding Natural Disasters as Risks in Supply Chain Management through Web Data Analysis,†Int. J. Comput. Commun. Eng., vol. 4, no. 2, pp. 126–133, 2015.

Z. Lu and K. Grauman, “Story-driven summarization for egocentric video,†Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 2714–2721, 2013.

A. Fuji and T. Ishikawa, “A System for Summarizing and Visualizing Arguments in Subjective Documents: Toward Supporting Decision Making,†in Proceedings of the Workshop on Sentiment and Subjectivity in Text, 2006, vol. 69–72, no. July, pp. 15–22.

D. R. Radev, H. Jing, and M. Budzikowska, “Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies,†Inf. Process. Manag. 40.6 919-938., vol. 40, no. 6, p. 10, 2000.

M. Damova and I. Koychev, “Query-Based Summarization : A survey.â€

J. M. Conroy and J. G. Stewart, “CLASSY Query-Based Multi-Document Summarization,†Proc. DUC2005, 2005.

Q. M. Summarization, S. D. Silva, N. Joshi, S. Rao, S. Venkatraman, and S. Shrawne, “Improved Algorithms for Document Classification &,†vol. 3, no. 4, 2011.

D. J. Brenes, D. Gayo-Avello, and K. Pérez-González, “Survey and evaluation of query intent detection methods,†Proc. 2009 Work. Web Search Click Data - WSCD ’09, pp. 1–7, 2009.

H. Daumé, “Bayesian Query-Focused Summarization,†2009.

L. Wang, H. Raghavan, V. Castelli, R. Florian, and C. Cardie, “A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization,†2016.

S. Gupta, A. Nenkova, and D. Jurafsky, “Measuring importance and query relevance in topic-focused multi-document summarization,†Proc. 45th Annu. Meet. ACL Interact. Poster Demonstr. Sess. - ACL ’07, no. June, p. 193, 2007.

C. Y. Lin, “Rouge: A package for automatic evaluation of summaries,†Proc. Work. text Summ. branches out (WAS 2004), no. 1, pp. 25–26, 2004.

A. Abdi, N. Idris, R. M. Alguliyev, and R. M. Aliguliyev, “Query-based multi-documents summarization using linguistic knowledge and content word expansion,†Soft Comput., vol. 21, no. 7, pp. 1785–1801, 2017.

F. Tao et al., “Multi-Dimensional, Phrase-Based Summarization in

Text Cubes,†Data Eng., p. 74, 2016.