Document summarization involves reducing a text document into a short set of phrases or sentences that convey the main meaning of the text. In digital libraries, summaries can be used as concise descriptions which the user can read for a rapid comprehension of the retrieved documents. Most of the existing approaches rely on the classification algorithms which tend to generate “crisp” summaries, where the phrases are considered equally relevant and no information on their degree of importance or factor of significance is provided. Motivated by this, we present a probabilistic relational data mining method to model preference relations on sentences of document images. Preference relations are then used to rank the sentences which will form the final summary. We empirically evaluate the method on real document images.
Ranking Sentences for Keyphrase Extraction: A Relational Data Mining Approach
CECI, MICHELANGELO;LOGLISCI, CORRADO;
2014-01-01
Abstract
Document summarization involves reducing a text document into a short set of phrases or sentences that convey the main meaning of the text. In digital libraries, summaries can be used as concise descriptions which the user can read for a rapid comprehension of the retrieved documents. Most of the existing approaches rely on the classification algorithms which tend to generate “crisp” summaries, where the phrases are considered equally relevant and no information on their degree of importance or factor of significance is provided. Motivated by this, we present a probabilistic relational data mining method to model preference relations on sentences of document images. Preference relations are then used to rank the sentences which will form the final summary. We empirically evaluate the method on real document images.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
