Comparison of Graph-based and Term Weighting Method for Automatic Summarization of Online News

Rumagit, Reinert Yosua; Setiyawati, Nina; Bangkalang, Dwi Hosanna

doi:10.1016/j.procs.2019.08.220

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…In MA methods, wordless analysis supplements all those words which are not present in the dictionary. Wordless morphology uses the regularities according to inflections and grammatical meanings [21], [37], [44]. The principle of this morphology relies on analyzing the end of a word for predicting its morphological features.…”

Section: B Dictionary-based Approachesmentioning

confidence: 99%

“…The principle of this morphology relies on analyzing the end of a word for predicting its morphological features. [26], [37]. The English language MA is based on the use of finite state machines and finite state transducer [23], [24], [31].…”

Section: B Dictionary-based Approachesmentioning

confidence: 99%

“…CS is the right choice when attributes are high-dimensional, especially in information retrieval and text analysis [37], [38], [42].…”

Section: G Cosine Similaritymentioning

confidence: 99%

“…to the word embeddings as Word2Vec model [31], [37], [44]. We converted the unstructured text corpus into the three parallel reduced corpuses.…”

Section: B Making Multiple Summarized Parallel Corpusesmentioning

confidence: 99%

“…Similarly, the semantic assessment by latent analysis [19], [27], [39], Markov-models, and graph-oriented supervised and unsupervised techniques are under investigation [4], [16], [27]. In short, the extractive-summarization is a key area of research as per the current literature analysis [2], [18], [29], [37]. For simplifying the extractive summarization, the sentence selection process considers the summary-worthiness.…”

Section: Introductionmentioning

confidence: 99%

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Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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In natural language processing, text summarization is an important application used to extract desired information by reducing large text. Existing studies use keyword-based algorithms for grouping text, which do not give the documents' actual theme. Our proposed dynamic corpus creation mechanism combines metadata with summarized extracted text. The proposed approach analyzes the mesh of multiple unstructured documents and generates a linked set of multiple weighted nodes by applying multistage Clustering. We have generated adjacency graphs to link the clusters of various collections of documents. This approach comprises of ten steps: pre-processing, making multiple corpuses, first stage clustering, creating sub-corpuses, interlinking sub-corpuses, creating page rank keyword dictionary of each sub-corpus, second stage clustering, path creation among clusters of sub-corpuses, text processing by forward and backward propagation for results generation. The outcome of this technique consists of interlinked subcorpuses through clusters. We have applied our approach to a News dataset, and this interlinked corpus processing follows step by step clustering to search the most relevant parts of the corpus with less cost, time, and improve content detection. We have applied six different metadata processing combinations over multiple text queries to compare results during our experimentation. The comparison results of text satisfaction show that Page-Rank keywords give 38% related text, single-stage Clustering gives 46%, twostage Clustering gives 54%, and the proposed technique gives 67% associated text. Furthermore, this approach covers/searches the relevant data with a range of most to less relevant content. It provides the systematic query-relevant corpus processing mechanism, which automatically selects the most relevant subcorpus through dynamic path selection. We used the SHAP model to evaluate the proposed technique, and our evaluation results proved that the proposed mechanism improved text processing. Moreover, combining text summarization features, shown satisfactory results compared to the summaries generated by general models of abstractive & extractive summarization.

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Section: B Dictionary-based Approachesmentioning

confidence: 99%