ISTE/John Wiley, 2014. — 376 p.
Text summarization, the reduction of a text to its essential content, is a task that requires linguistic competence, world knowledge, and intelligence. Automatic text summarization, the production of summaries by computers is therefore a very difficult task. One may wonder whether machines would ever be able to produce summaries which are indistinguishable from human summaries, a kind of Turing test and a motivation to advance the state of the art in natural language processing. Text summarization algorithms have many times ignored the cognitive processes and the knowledge that go into text understanding and which are essential to properly summarize.
In
Automatic Text Summarization, Juan-Manuel Torres-Moreno offers a comprehensive overview of methods and techniques used in automatic text summarization research from the first attempts to the most recent trends in the field (e.g. opinion and tweet summarization).
FoundationsWhy Summarize Texts?
Automatic Text Summarization: Some Important Concepts
Single-Document Summarization
Guided Multi-Document Summarization
Emerging SystemsMulti and Cross-Lingual Summarization
Source and Domain-Specific Summarization
Text Abstracting
Evaluating Document Summaries
A: Information Retrieval, NLP and ATS
B: Automatic Text Summarization Resources