LexRank: Graph-Based Lexical Centrality as Salience in Text Summarization

Metadata

• Author: cmu.edu
• Full Title: LexRank: Graph-Based Lexical Centrality as Salience in Text Summarization
• URL: https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume22/erkan04a-html/erkan04a.html

Highlights

Extractive summarization produces summaries by choosing a subset of the sentences in the original document(s). This contrasts with abstractive summarization, where the information in the text is rephrased. Although summaries produced by humans are typically not extractive, most of the summarization research today is on extractive summarization. Purely extractive summaries often give better results compared to automatic abstractive summaries. This is due to the fact that the problems in abstractive summarization, such as semantic representation, inference and natural language generation, are relatively harder compared to a data-driven approach such as sentence extraction (View Highlight)