We report on ITC-irst participation at Task 1 (very short document summaries) at DUC-2004. We propose to exploit a keyphrase extraction methodology in order to identify relevant terms in the document. The LAKE algorithm first considers a number of linguistic features to extract a list of well motivated candidate keyphrases, then uses a machine learning framework to select significant keyphrases for a document. With respect to other approaches to keyphrase extraction, LAKE makes use of linguistic processors such as multiword and named entities recognition, which are not usually exploited
Keyphrase Extraction for Summarization Purposes: The LAKE System at DUC-2004
D'Avanzo, Ernesto;Magnini, Bernardo;
2004-01-01
Abstract
We report on ITC-irst participation at Task 1 (very short document summaries) at DUC-2004. We propose to exploit a keyphrase extraction methodology in order to identify relevant terms in the document. The LAKE algorithm first considers a number of linguistic features to extract a list of well motivated candidate keyphrases, then uses a machine learning framework to select significant keyphrases for a document. With respect to other approaches to keyphrase extraction, LAKE makes use of linguistic processors such as multiword and named entities recognition, which are not usually exploitedFile in questo prodotto:
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