A Study of Abstractive Summarization Using Semantic Representations and Discourse Level Information

摘要

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 PageR-ank 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.