A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Advantages of query biased summaries in information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
ACM SIGIR Forum
Implicit user profiling for on demand relevance feedback
Proceedings of the 9th international conference on Intelligent user interfaces
An analysis of the AskMSR question-answering system
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Bayesian query-focused summarization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
NLTK: the natural language toolkit
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Unsupervised Multilingual Sentence Boundary Detection
Computational Linguistics
Eye-mouse coordination patterns on web search results pages
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using gaze-based feedback on the subdocument level
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Exploring mouse movements for inferring query intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Predicting the readability of short web summaries
Proceedings of the Second ACM International Conference on Web Search and Data Mining
What do you see when you're surfing?: using eye tracking to predict salient regions of web pages
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Segment-level display time as implicit feedback: a comparison to eye tracking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Text summarization model based on maximum coverage problem and its variant
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Web search result summarization: title selection algorithms and user satisfaction
Proceedings of the 18th ACM conference on Information and knowledge management
Towards predicting web searcher gaze position from mouse movements
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Inferring query intent from reformulations and clicks
Proceedings of the 19th international conference on World wide web
Using topic themes for multi-document summarization
ACM Transactions on Information Systems (TOIS)
The good, the bad, and the random: an eye-tracking study of ad quality in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
No clicks, no problem: using cursor movements to understand and improve search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Find it if you can: a game for modeling different types of web search success using interaction data
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Evaluating web search result summaries
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Proceedings of the 21st international conference on World Wide Web
User see, user point: gaze and cursor alignment in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving searcher models using mouse cursor activity
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Text selections as implicit relevance feedback
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Mining touch interaction data on mobile devices to predict web search result relevance
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Discovering common motifs in cursor movement data for improving web search
Proceedings of the 7th ACM international conference on Web search and data mining
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Query-biased search result summaries, or "snippets", help users decide whether a result is relevant for their information need, and have become increasingly important for helping searchers with difficult or ambiguous search tasks. Previously published snippet generation algorithms have been primarily based on selecting document fragments most similar to the query, which does not take into account which parts of the document the searchers actually found useful. We present a new approach to improving result summaries by incorporating post-click searcher behavior data, such as mouse cursor movements and scrolling over the result documents. To achieve this aim, we develop a method for collecting behavioral data with precise association between searcher intent, document examination behavior, and the corresponding document fragments. In turn, this allows us to incorporate page examination behavior signals into a novel Behavior-Biased Snippet generation system (BeBS). By mining searcher examination data, BeBS infers document fragments of most interest to users, and combines this evidence with text-based features to select the most promising fragments for inclusion in the result summary. Our extensive experiments and analysis demonstrate that our method improves the quality of result summaries compared to existing state-of-the-art methods. We believe that this work opens a new direction for improving search result presentation, and we make available the code and the search behavior data used in this study to encourage further research in this area.