Automatic Text Summarization by Juan-Manuel Torres-Moreno

By Juan-Manuel Torres-Moreno

This new textbook examines the motivations and the various algorithms for automated record summarization (ADS). We played a contemporary state-of-the-art. The e-book indicates the most difficulties of advertisements, problems and the recommendations supplied by means of the group. It provides fresh advances in advertisements, in addition to present purposes and tendencies. The techniques are statistical, linguistic and symbolic. numerous exemples are integrated for you to make clear the theoretical concepts.  The books at the moment to be had within the region of automated record Summarization should not contemporary. robust algorithms were built in recent times that come with a number of functions of advertisements. the improvement of modern expertise has impacted at the improvement of algorithms and their functions. the big use of social networks and the hot kinds of the expertise calls for the difference of the classical equipment of textual content summarizers. it is a new textbook on computerized textual content Summarization, in response to educating fabrics utilized in or one-semester classes. It provides a broad state-of-art and describes the recent platforms at the topic. earlier computerized summarization books were both collections of specialised papers, otherwise authored books with just a bankruptcy or dedicated to the sector as an entire. In different hand, the vintage books at the topic are usually not contemporary.

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7). 1. Sentence compression The idea behind sentence compression is simple: to eliminate all the non-essential information in a sentence while preserving grammaticality. Turning this idea into an algorithm is, however, a whole other matter, and no method has ever found a definitive solution to this problem. There is a range of applications for sentence compression: automatic title generation, generating subtitles for the media, displaying information on mobile devices and so on. Automatic summarization is also concerned with sentence compression.

7). 2). 3). 2. Intermediate-level algorithms Intermediate-level algorithms use linguistic information that is more sophisticated than surface-level algorithms but less sophisticated than deep parsing. One intermediate-level technique is lexical chain recognition. Lexical chains are sequences of words connected by lexical semantic relations [BAR 97, SIL 00]. In general, producing an automatic summary involves four stages. First, the original text is divided into segments and then lexical chains are constructed.

E. a plain text document, to be transformed into an object with minimal linguistic features, such as words and sentences. Preprocessing has two objectives: word normalization and lexicon reduction in a text. 1), because the space of 24 Automatic Text Summarization representation is considerably reduced and becomes easier to manage. Therefore, preprocessing is essential, as it provides summarization systems with a clean and adequate representation of the source documents. ) 2; – filtering stopwords, etc 3.

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