Extractive Text Summarization Using Sentence Ranking

Madhuri, J.; Kumar, R. Ganesh

doi:10.1109/icondsc.2019.8817040

Cited by 91 publications

(24 citation statements)

References 5 publications

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“…This could make the algorithm confused with the location which is the address of the patient. In the future work, Text Summarization [12]- [15], Word Segmentation [16], [17], and Word Embedding [18]- [21] algorithm will be applied to improve the program. In addition, it will be incorporated with an open toolbox developed in [22] for generating nation-wide Covid-19 map to provide overall outlook of the situation in the country.…”

Section: Results and Analysismentioning

confidence: 99%

An Approach for SARS-CoV-2 Infected Cases Report Analysis

Chung

Nguyen

et al. 2020

2020 International Conference on Computational Intelligence (ICCI)

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The information about Coronavirus disease 2019 (COVID-19), especially about infected cases in every country is very urgent. In this paper, an algorithm to analyze the COVID-19 infected case reports is introduced. Fifty-two (52) reported cases from LuatVietnam -a reputable Vietnamese online newspaper -were taken as input. The retrieved data were analyzed and classified. The analysis output was saved into a CSV file showing the essential extracted information about infected cases. Each output row contains Patient ID, Gender, Age, Address and Status. Based on the tested results, the algorithm achieved the accuracy of 86.67% with the average processing time per patient of 0.103 milliseconds.

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Section: Results and Analysismentioning

confidence: 99%

An Approach for SARS-CoV-2 Infected Cases Report Analysis

Chung

Nguyen

et al. 2020

2020 International Conference on Computational Intelligence (ICCI)

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“…The underlying principle of Luhn's approach was that the salient points of an author's argument can be identified through the statistical analysis of the most frequent words and phrases occurring in texts. The hypothesis was that authors generally use important words more frequently throughout a paper and this can be conveniently used as a predictor for selecting the sentences with more repetition of the keywords and extracting them to generate an automatic summary [27][28][29].…”

Section: Previous Workmentioning

confidence: 99%

An Evaluation of Automatic Text Summarization of News Articles: The Case of Three Online Arabic Text Summary Generators

Alliheibi¹,

Omar²,

Al-Horais³

2021

IJACSA

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Digital news platforms and online newspapers have multiplied at an unprecedented speed, making it difficult for users to read and follow all news articles on important, relevant topics. Numerous automatic text summarization systems have thus been developed to address the increasing needs of users around the world for summaries that reduce reading and processing time. Various automatic summarization systems have been developed and/or adapted in Arabic. The evaluation of automatic summarization performance is as important as the summarization process itself. Despite the importance of assessing summarization systems to identify potential limitations and improve their performance, very little has been done in this respect on systems in Arabic. Therefore, this study evaluated three text summarizers AlSummarizer, LAKHASLY, and RESOOMER using a corpus built of 40 news articles. Only articles written in Modern Standard Arabic (MSA) were selected as this is the formal and working language of Arab newspapers and news networks. Three expert examiners generated manual summaries and examined the linguistic consistency and relevance of the automatic summaries to the original news articles by comparing the automatic summaries to the manual (human) summaries. The scores for the three automatic summarizers were very similar and indicated that their performance was not satisfactory. In particular, the automatic summaries had serious problems with sentence relevance, which has negative implications for the reliability of such systems. The poor performance of Arabic summarizers can mainly be attributed to the unique morphological and syntactic characteristics of Arabic, which differ in many ways from English and other Western languages (the original language/s of automatic summarizers), and are critical in building sentence relevance and coherence in Arabic. Thus, summarization systems should be trained to identify discourse markers within the texts and use these in the generation of automatic summaries. This will have a positive impact on the quality and reliability of text summarization systems. Arabic summarization systems need to incorporate semantic approaches to improve performance and construct more coherent and meaningful summaries. This study was limited to news articles in MSA. However, the findings of the study and their implications can be extended to other genres, including academic articles.

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“…J.N.Madhuri and Ganesh Kumar.R,Extractive [1] document. The input text converted in the short form using the idea of sentence extraction method.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Text Summarization from Unstructured Text using Natural Language Processing

Aswani¹

2020

IJETER

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Text summarization is one which creates short, concrete, fluent data from a longer document. Text summarization is of two types: one is abstractive, and another is extractive. In this paper, automatic text summarization is done using extractive summarization. For the idea of generating deserved summary, the unstructured text is first pre-processing which includes -tokenization, parts of speech, chunking. After pre-processing, feature extraction process take place which uses scoring method and a novel reinforcement learning based training algorithm to extract the data, which is greater than threshold value, that extract data is stored for final summary.

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Extractive Text Summarization Using Sentence Ranking

Cited by 91 publications

References 5 publications

An Approach for SARS-CoV-2 Infected Cases Report Analysis

An Approach for SARS-CoV-2 Infected Cases Report Analysis

An Evaluation of Automatic Text Summarization of News Articles: The Case of Three Online Arabic Text Summary Generators

Automatic Text Summarization from Unstructured Text using Natural Language Processing

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