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.