Topic Oriented Multi-document Summarization Using LSA, Syntactic and Semantic Features

Anjaneyulu, M.; Sarma, Samar Sen; Reddy, P. Vijaya Pal; Chander, K. Prem; Nagaprasad, S.

doi:10.1007/978-981-13-2354-6_50

Cited by 1 publication

(2 citation statements)

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“…Some of the other studies use different shallow features [7], [8]. In addition, some works try to represent the documents with semantic sentence features via probabilistic latent semantic analysis [9], latent semantic analysis (LSA) [10] and non-negative matrix factorisation [11], which explore the relationships between a set of sentences and words by generating a set of subjects related to sentences and words.…”

Section: Literature Review For Atmentioning

confidence: 99%

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Single Document Summarization Based on Grey Wolf Optimization

Sartakhti

Yoosofan

Fatehi

et al. 2020

GJCS

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The amazing growth of online services has caused an information explosion issue. Text summarisation is condensing the text into a small version and preserving its overall concept. Text summarisation is an important way to extract significant information from documents and offer that information to the user in an abbreviated form while preserving its major content. For human beings, it is very difficult to summarise large documents. To do this, this paper uses some sentence features and word features. These features assign scores to all the sentences. In this paper, we combine these features by Grey Wolf Optimiser (GWO). Optimisation of features gives better results than using individual features. This is the first attempt to show the performance of GWO for Persian text summarisation. The proposed method is compared with the genetic algorithm and the evolutionary strategy. The results show that our model will be useful in this research area. Keywords: Text summarisation, genetic algorithm, sentence, score function, evolutionary strategy.

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Section: Literature Review For Atmentioning

confidence: 99%

“…2.1.1.8. 8 -LSA-based feature: The goal of this feature that is based on [10]is to select sentences that are related to all significant subjects of the document. At first, in this feature must is carried out in the singular value decomposition procedure on a term-sentence matrix of the document.…”

Section: Sentence Featuresmentioning

confidence: 99%