By Juan-Manuel Torres-Moreno
Textual info within the type of electronic records quick accumulates to create large quantities of knowledge. nearly all of those files are unstructured: it really is unrestricted textual content and has no longer been equipped into conventional databases. Processing files is hence a perfunctory activity, normally as a result of a scarcity of criteria. It has hence turn into super tough to enforce computerized textual content research initiatives. computerized textual content Summarization (ATS), by way of condensing the textual content whereas retaining proper details, can assist to approach this ever-increasing, difficult-to-handle, mass of information.
This e-book examines the motivations and varied algorithms for ATS. the writer provides the hot state-of-the-art sooner than describing the most difficulties of ATS, in addition to the problems and strategies supplied by means of the group. The ebook presents fresh advances in ATS, in addition to present purposes and developments. The ways are statistical, linguistic and symbolic. a number of examples also are integrated to be able to make clear the theoretical concepts.
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Extra resources for Automatic Text Summarization (Iste)
It focuses on the relation between signiﬁers, like words, phrases, signs, and symbols, and what they stand for, their denotation. org/wiki/Semantics). 14. Scripts and frames commonly found in the literature for artiﬁcial intelligence: [SCH 77]. 15. macs. php. Automatic Text Summarization: Some Important Concepts 37 F RUMP’s knowledge had to be coded by hand, an enormous, arduous task, which, moreover, relied on human experts. This leads us to ask an obvious question: how can scripts for new domains be automatically learned?
There are multiple applications for ATS: news, RSS, email, web page and blog summarization; specialized document, meeting and report summarization; biographic extracts; opinion summarization; automatic title generation etc. 6. 00593. The reader, of course, has the ﬁnal say and can judge for himself or herself the relevance of the summaries produced. 15. 2. The F RESA score falls between [0, 1]: 0 indicates a bad summary and 1 indicates that the summary is very similar to the source text. Why Summarize Texts?
Categorizing automatic summaries Summaries have two main functions: direct functions and indirect functions. The direct functions provide an overview of the document Why Summarize Texts? 11 (essential information), update the user (update summary), eliminate language barriers (multi- and cross-lingual summary) or facilitate information retrieval (IR). The indirect functions enable documents to be classiﬁed and indexed, keywords to be extracted and so on. Summaries can be categorized according to different sets of criteria, such as their function, type and document source, etc.