Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Concept decompositions for large sparse text data using clustering
Machine Learning
Iterative Clustering of High Dimensional Text Data Augmented by Local Search
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Multidocument summarization: An added value to clustering in interactive retrieval
ACM Transactions on Information Systems (TOIS)
HLT '01 Proceedings of the first international conference on Human language technology research
Tracking and summarizing news on a daily basis with Columbia's Newsblaster
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Expert Systems with Applications: An International Journal
Clustering of document collection - A weighting approach
Expert Systems with Applications: An International Journal
A Gradual Combination of Features for Building Automatic Summarisation Systems
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Integrating Document Clustering and Multidocument Summarization
ACM Transactions on Knowledge Discovery from Data (TKDD)
A query-based multi-document sentiment summarizer
Proceedings of the 20th ACM international conference on Information and knowledge management
Text summarisation in progress: a literature review
Artificial Intelligence Review
Exploring clustering for multi-document arabic summarisation
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Predicting the ratings of multimedia items for making personalized recommendations
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Multiple documents summarization based on evolutionary optimization algorithm
Expert Systems with Applications: An International Journal
Application of Text Summarization techniques to the Geographical Information Retrieval task
Expert Systems with Applications: An International Journal
Editorial: COMPENDIUM: A text summarization system for generating abstracts of research papers
Data & Knowledge Engineering
Extractive single-document summarization based on genetic operators and guided local search
Expert Systems with Applications: An International Journal
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Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel integrated information retrieval system-the Query, Cluster, Summarize (QCS) system-which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of methods in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test sets from the Document Understanding Conferences (DUC) as measured by the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence "trimming" and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules.