By Juan-Manuel Torres-Moreno
This new textbook examines the motivations and different algorithms for computerized record summarization (ADS). We played a up to date cutting-edge. The ebook exhibits the most difficulties of advertisements, problems and the options supplied by means of the neighborhood. It offers contemporary advances in advertisements, in addition to present functions and tendencies. The techniques are statistical, linguistic and symbolic. a number of exemples are incorporated on the way to make clear the theoretical concepts. The books presently on hand within the quarter of computerized record Summarization usually are not fresh. robust algorithms were constructed in recent times that come with numerous functions of advertisements. the improvement of modern expertise has impacted at the improvement of algorithms and their purposes. the big use of social networks and the recent kinds of the expertise calls for the variation of the classical equipment of textual content summarizers. it is a new textbook on computerized textual content Summarization, in line with educating fabrics utilized in or one-semester classes. It provides a large state-of-art and describes the hot platforms at the topic. earlier automated summarization books were both collections of specialised papers, in any other case authored books with just a bankruptcy or dedicated to the sector as a complete. In different hand, the vintage books at the topic will not be fresh.
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In this model, word order is not important. It is known as the bagof-words model. Each word (term) is given a weight ω, which measures its importance in the document. 2): 6. See, for instance, The Comprehensive Perl Archive Network (CPAN)’s language identiﬁcation algorithms: http://search. org/ pod/Lingua::Identify. 7. de/schmid/ tools/TreeTagger/. Automatic Text Summarization: Some Important Concepts 27 Binary: ωμ,j equals 1 if the term j is in the sentence μ, and 0 if not; Frequency: number of occurrences of the term j in a sentence μ (term frequency, tf): ωμ,j = tfμ,j Corrective: using a correction frequency function to take into account the distribution of the word in sentences (inverse document frequency, idf).
Once the discursive structure of the text is created, an algorithm is applied that weights and orders each element of the tree-like structure of the discourse (the higher the element in the structure, the higher its weighting). Sentences with the highest weight are selected for the summary. More or fewer elements are chosen depending on the desired length of the summary, but they always appear in the order determined by the algorithm. Recently, deep parsing approaches applied to specialized domain texts have come into existence, such as D ISICOSUM [CUN 07b, CUN 08], which uses RST and speciﬁc rules to summarize texts in the biomedicine domain (see Chapter 6).
In English, there is no ambiguity between the meaning of summary, and the meaning of abstract or extract: – a summary is a reduced representation that keeps the essential content of a text; – an abstract is a summary produced by reformulating sentences; – an extract is a summary produced by extracting sentences from the source text. However, in languages such as French or Spanish, misuse of the word “résumé” (or “resumen” in Spanish) has led to the meanings of summary and abstract being conﬂated and resulting in ambiguity.