ExB Text Summarizer

Thomas, Stefan; Beutenmüller, Christian; Puente, Xose de la; Remus, Robert; Bordag, Stefan

doi:10.18653/v1/w15-4637

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…The rich and intricate morphological and syntactic flexibility of Arabic is widely known [32]. The preprocessing stage is essentially the same for all languages and often entails normalization, tokenization, POS tagging, stemming/lemmatization, and stop-word removal [33][34][35]. Since most texts produced in Arabic and saved in electronic form do not have diacritical marks at first, the system deals with Arabic texts without them.…”

Section: Data Pre-processingmentioning

confidence: 99%

Extractive Arabic Text Summarization-Graph-Based Approach

AL-Khassawneh

Hanandeh

2023

Electronics

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With the noteworthy expansion of textual data sources in recent years, easy, quick, and precise text processing has become a challenge for key qualifiers. Automatic text summarization is the process of squeezing text documents into shorter summaries to facilitate verification of their basic contents, which must be completed without losing vital information and features. The most difficult information retrieval task is text summarization, particularly for Arabic. In this research, we offer an automatic, general, and extractive Arabic single document summarizing approach with the goal of delivering a sufficiently informative summary. The proposed model is based on a textual graph to generate a coherent summary. Firstly, the original text is converted to a textual graph using a novel formulation that takes into account sentence relevance, coverage, and diversity to evaluate each sentence using a mix of statistical and semantic criteria. Next, a sub-graph is built to reduce the size of the original text. Finally, unwanted and less weighted phrases are removed from the summarized sentences to generate a final summary. We used Recall-Oriented Research to Evaluate Main Idea (RED) as an evaluative metric to review our proposed technique and compare it with the most advanced methods. Finally, a trial on the Essex Arabic Summary Corpus (EASC) using the ROUGE index showed promising results compared with the currently available methods.

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Section: Data Pre-processingmentioning

confidence: 99%

Extractive Arabic Text Summarization-Graph-Based Approach

AL-Khassawneh

Hanandeh

2023

Electronics

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“…The following Architecture for QA corpus is shown in Figure 1. In paper [18], [19] & [20] discuss on query is pre-processed using tokenization, stop words removal and stemming to extract keywords. Question type considered are what, when, why, which are trained using question classifier.…”

Section: Proposed System Architecturementioning

confidence: 99%

Text Summarization using QA Corpus for User Interaction Model QA System

Karpagam¹,

Saradha²,

Manikandan³

et al. 2020

IJEME

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Document summarization is capable of generating user query relevant, precise summaries from the original document for user needs. To reduce the response time summary generation, QA corpus is built for similar questions and answer with help of learning model. It has been trained and tested by Quora duplicate and Yahoo! Answer datasets. The large QA corpus has been dynamically clustered with semantic features paves a way for efficient document's retrieval. Answers are produced from datasets or generate summaries for unanswerable from the available sources. Results obtained from statistical significance test with hypothesis testing and evaluation with standard metrics proves the significant improvement in generating text summarization using QA corpus. The outcome is better in the producing close proximity of answers for the given user query.

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“…Then the score of each sentence is assigned in respect to its distance from the clusters' representatives. For example, Thomas et al(2015) used a graph-based procedure where each node of the graph represents a sentence and the edges' weights reflect the similarity between the connected nodes. Next, a PageRank/TextRank algorithm is applied 2015) Principal Component Analysis (PCA) was used to project the sentences into a lower-dimension space.…”

Section: Sentence-based Summarizationmentioning

confidence: 99%

A topic-based sentence representation for extractive text summarization

Gialitsis¹,

Pittaras²,

Stamatopoulos³

et al. 2019

Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources Associated With RANLP 2019

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We examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of sentence selection as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings for classification and modelling. A preliminary investigation via a wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topicbased representations can prove beneficial to the extractive summarization process, compared to a TF-IDF baseline, with Quadratic Discriminant Analysis and Gradient Boosting providing the best results for micro and macro F1 score, respectively.

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ExB Text Summarizer

Cited by 8 publications

References 13 publications

Extractive Arabic Text Summarization-Graph-Based Approach

Extractive Arabic Text Summarization-Graph-Based Approach

Text Summarization using QA Corpus for User Interaction Model QA System

A topic-based sentence representation for extractive text summarization

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