Automatic Text Summarization by Juan-Manuel Torres-Moreno

By Juan-Manuel Torres-Moreno

This new textbook examines the motivations and the several algorithms for computerized rfile summarization (ADS). We played a contemporary cutting-edge. The e-book indicates the most difficulties of advertisements, problems and the options supplied via the neighborhood. It provides fresh advances in advertisements, in addition to present purposes and tendencies. The techniques are statistical, linguistic and symbolic. a number of exemples are incorporated with a view to make clear the theoretical concepts.  The books presently on hand within the region of automated record Summarization should not contemporary. robust algorithms were constructed in recent times that come with a number of functions of advertisements. the advance of modern expertise has impacted at the improvement of algorithms and their functions. the large use of social networks and the hot sorts 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 line with instructing fabrics utilized in or one-semester classes. It offers a broad 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 sphere as a complete. In different hand, the vintage books at the topic should not fresh.

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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. In fact, sentence compression is considered a resource for optimizing automatic summarization [MOL 10a], but also a sort of summary itself, in which the compression rate can reach 33% [YOU 06].

2 shows a basic approximation of the process of generating automatic text summaries. The source text (single or multidocument) is processed by a summarization system according to a query and certain parameters, such as the compression rate, the desired type of summary and the desired function. The system produces an extract, an abstract or a summary via compression. The summarization module in the center of the figure will be discussed in more detail in the following chapter. 4. Applications of automatic text summarization There are countless applications of automatic text summarization.

Org/wiki/Naive_Bayes_classifier). Automatic Text Summarization: Some Important Concepts 33 in summaries. The optimal position policy (OPP) [LIN 97] provides empirical validation for the importance of the position of the sentence in a discourse structure. OPP employs sentence weighting using ML. Alonso and Fuentes [ALO 03] developed an extraction summarization system that uses an ML approach followed by sentence compression (sentences are extracted using handwritten rules) to obtain short and grammatically correct sentences.

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