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Web search result summarization: title selection algorithms and user satisfaction

Published: 02 November 2009 Publication History

Abstract

Eye tracking experiments have shown that titles of Web search results play a crucial role in guiding a user's search process. We present a machine-learned algorithm that trains a boosted tree to pick the most relevant title for a Web search result. We compare two modeling approaches: i) using absolute editorial judgments and ii) using pairwise preference judgments. We find that the pairwise modeling approach gives better results in terms of three offline metrics. We present results of our models in four regions. We also describe a hybrid user satisfaction evaluation process -- search success -- that combines page relevance and user click behavior, and show that our machine-learned algorithm improves in search success.

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J. H. Friedman. Stochastic gradient boosting. Computational Statistics and Data Analysis, 38:367--378, 2001.
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R. Khan, D. Mease, and R. Patel. The impact of result abstracts on task completion time. In Proc. of WWW Workshop on Web Search Result Summarization and Presentation, 2009.
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  • (2023)A Recommender System for Recovering Relevant JavaScript Packages from Web Repositories2023 IEEE 20th International Conference on Software Architecture (ICSA)10.1109/ICSA56044.2023.00024(175-185)Online publication date: Mar-2023
  • (2013)Improving search result summaries by using searcher behavior dataProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval10.1145/2484028.2484093(13-22)Online publication date: 28-Jul-2013
  • (2011)Learning Query Patterns by Using Wikipedia Articles as Supervised Data to Retrieve Web Pages for Multi-document SummarizationTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.26.36626(366-375)Online publication date: 2011
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      cover image ACM Conferences
      CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management
      November 2009
      2162 pages
      ISBN:9781605585123
      DOI:10.1145/1645953
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 02 November 2009

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      Author Tags

      1. machine learning
      2. user satisfaction
      3. web summarization

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      View all
      • (2023)A Recommender System for Recovering Relevant JavaScript Packages from Web Repositories2023 IEEE 20th International Conference on Software Architecture (ICSA)10.1109/ICSA56044.2023.00024(175-185)Online publication date: Mar-2023
      • (2013)Improving search result summaries by using searcher behavior dataProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval10.1145/2484028.2484093(13-22)Online publication date: 28-Jul-2013
      • (2011)Learning Query Patterns by Using Wikipedia Articles as Supervised Data to Retrieve Web Pages for Multi-document SummarizationTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.26.36626(366-375)Online publication date: 2011
      • (2011)Enhanced results for web searchProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2010014(725-734)Online publication date: 24-Jul-2011
      • (2010)Learning web query patterns for imitating Wikipedia articlesProceedings of the 23rd International Conference on Computational Linguistics: Posters10.5555/1944566.1944707(1229-1237)Online publication date: 23-Aug-2010

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