Title
A study of abstractive summarization using semantic representations and discourse level information
Date Issued
01 January 2017
Access level
metadata only access
Resource Type
conference paper
Author(s)
Publisher(s)
Springer Verlag
Abstract
The present work proposes an exploratory study of abstractive summarization integrating semantic analysis and discursive information. Firstly, we built a conceptual graph using some lexical resources and Abstract Meaning Representation (AMR). Secondly, we applied PageRank algorithm to get the most relevant concepts. Also, we incorporated discursive information of Rethorical Structure Theory (RST) into the PageRank to improve the relevant concepts identification. Finally, we made some rules over the relevant concepts and applied SimpleNLG to make the summaries. This study was performed on the corpus of DUC 2002 and the results showed a F1-measure of 24% in Rouge-1 when AMR and RST were used, proving their usefulness in this task.
Start page
482
End page
490
Volume
10415 LNAI
Language
English
OCDE Knowledge area
Ling眉铆stica
Inform谩tica y Ciencias de la Informaci贸n
Subjects
Scopus EID
2-s2.0-85028669414
ISBN
9783319642055
ISSN of the container
03029743
DOI of the container
10.1007/978-3-319-64206-2_54
Conference
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Sources of information:
Directorio de Producci贸n Cient铆fica
Scopus