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.

Autore Pugliese

• Loglisci Corrado , Ceci Michelangelo

Tutti gli autori

• LOGLISCI C.;CECI M.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2014

ISSN

1877-0509

ISBN

Non Disponibile

Numero di citazioni Wos

4

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

6

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile