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Communication Dans Un Congrès Année : 2000

Interactive Learning for Text Summarization

Résumé

This paper describes a query-relevant text summary system based on interactive learning. The system proceeds in two steps, it first extracts the most relevant sentences of a document with regard to a user query using a classical tf-idf term weighting scheme, it then learns the user feedback in order to improve its performances. Learning operates at two levels: query expansion and sentence scoring.
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Dates et versions

hal-01573441 , version 1 (09-08-2017)

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  • HAL Id : hal-01573441 , version 1

Citer

Massih-Reza Amini. Interactive Learning for Text Summarization. PKDD/MLTIA Workshop on Machine Learning and Textual Information, Sep 2000, Lyon, France. ⟨hal-01573441⟩
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