Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Practical digital libraries: books, bytes, and bucks
Practical digital libraries: books, bytes, and bucks
Computer Evaluation of Indexing and Text Processing
Journal of the ACM (JACM)
Extended Boolean information retrieval
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Modern Information Retrieval
Managing Gigabytes: Compressing and Indexing Documents and Images
Managing Gigabytes: Compressing and Indexing Documents and Images
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
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One critical step in information retrieval is the skimming of the returned documents, considered as globally relevant by an Information retrieval system as responses to a user’s query. This skimming has generally to be done in order to find the parts of the returned documents which contain the information satisfying the user’s information need. This task may be particularly heavy when only small parts of the returned documents are related to the asked topic. Therefore, our proposition here is to substitute an automatic extraction and recomposition process in order to provide the user with synthetic documents, called here composite documents, made of parts of documents extracted from the set of documents returned as responses to a query. The composite documents are built in such a way that they summarize as concisely as possible the various aspects of relevant information for the query and which are initially scattered among the returned documents. Due to the combinatorial cost of the recomposition process, we use a genetic algorithm whose individuals are texts and that aims at optimizing a satisfaction criterion based on similarity. We have implemented several variants of the algorithm and we proposed an analysis of the first experimental results which seems promising for a preliminary work.