Abstract
As an essential part in web search, search snippets usually provide result previews for users to either gather useful information or make click-through decisions. In complex search scenarios, users may need to submit multiple queries to search systems until their information needs are satisfied. As user intents tend to be ambiguous, incorporating contextual information for user modeling has been proved effective in many session-level tasks. Therefore, the generation of search snippets may also benefit from the integration of context information. However, to our best knowledge, most existing snippet generation methods ignore user interaction and focus merely on the query content. Whether it is useful of exploiting session contexts to improve search snippets still remains inscrutable. To this end, we propose a snippet generation model which considers session contexts. The proposed method utilizes the query sequence as well as users’ interaction behaviors within a session to model users’ session-level information needs. We also adopt practical log-based search data to evaluate the performance of the proposed method. Experiment results based on both expert annotation and user preference test show the effectiveness of considering contextual information in search snippet generation.
This work is supported by Natural Science Foundation of China (Grant No. 61622208, 61532011, 61672311) and National Key Basic Research Program (2015CB358700).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
For all datasets, we train the network for 200 epochs because the training losses have converged stably within 200 epochs.
- 2.
- 3.
References
Wu, B., Xiong, C., Sun, M., et al.: Query suggestion with feedback memory network. In: Proceedings of the 2018 World Wide Web Conference, International World Wide Web Conferences Steering Committee, pp. 1563–1571 (2018)
Pennington, J., Socher, R., Manning, C.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Maxwell, D., Azzopardi, L., Moshfeghi, Y.: A study of snippet length and informativeness: behaviour, performance and user experience. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 35–144. ACM (2017)
Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004)
Wang, C., Jing, F., Zhang, L., et al.: Learning query-biased web page summarization. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 555–562. ACM (2007)
Penin, T., Wang, H., Tran, T., Yu, Y.: Snippet generation for semantic web search engines. In: Domingue, J., Anutariya, C. (eds.) ASWC 2008. LNCS, vol. 5367, pp. 493–507. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89704-0_34
Ko, Y., An, H., Seo, J.: An effective snippet generation method using the pseudo relevance feedback technique. In: Annual ACM Conference on Research and Development in Information Retrieval: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 711–712 (2007)
Sun, J.T., Shen, D., Zeng, H.J., et al.: Web-page summarization using clickthrough data. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 194–201. ACM (2005)
Chen, J., Mao, J., Liu, Y., Zhang, M., Ma, S.: Investigating query reformulation behavior of search users. In: Zhang, Q., Liao, X., Ren, Z. (eds.) CCIR 2019. LNCS, vol. 11772, pp. 39–51. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31624-2_4
Erkan, G., Radev, D.R.: Lexrank: Graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)
Mihalcea, R., Tarau, P.: Textrank: Bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–411 (2004)
Edmundson, H.P.: New methods in automatic extracting. J. ACM (JACM) 16(2), 264–285 (1969)
Tombros, A., Sanderson, M., Gray, P.: Advantages of query biased summaries in information retrieval. In: SIGIR, pp. 2–10 (1998)
Chuklin, A., Markov, I., Rijke, M.: Click models for web search. Synth. Lect. Inf. Concepts Retrieval Serv. 7(3), 1–115 (2015)
Chung, J., Gulcehre, C., Cho, K.H., et al.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014). arXiv:1412.3555
Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR, pp. 335–336 (1998)
Kumar, V., Khattar, D., Gairola, S., et al.: Identifying clickbait: A multi-strategy approach using neural networks. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1225–1228. ACM (2018)
Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)
Steinberger, J., Jezek, K.: Using latent semantic analysis in text summarization and summary evaluation. Proc. ISIM 4, 93–100 (2004)
Vanderwende, L., Suzuki, H., Brockett, C., et al.: Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion. Inf. Process. Manage. 43(6), 1606–1618 (2007)
Haghighi, A., Vanderwende, L.: Exploring content models for multi-document summarization. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 362–370. Association for Computational Linguistics (2009)
Ageev, M., Lagun, D., Agichtein, E.: Improving search result summaries by using searcher behavior data. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 13–22. ACM (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, J., Mao, J., Liu, Y., Zhang, M., Ma, S. (2020). Improving Search Snippets in Context-Aware Web Search Scenarios. In: Dou, Z., Miao, Q., Lu, W., Mao, J., Jia, G. (eds) Information Retrieval. CCIR 2020. Lecture Notes in Computer Science(), vol 12285. Springer, Cham. https://doi.org/10.1007/978-3-030-56725-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-56725-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-56724-8
Online ISBN: 978-3-030-56725-5
eBook Packages: Computer ScienceComputer Science (R0)