An extractive text summarization technique for Bengali document(s) using K-means clustering algorithm

Akter, Sumya; Siddika, Aysa; Uddin, Md. Palash; Hossain, Delowar; Roy, Shikhor Kumer; Afjal, Masud Ibn

doi:10.1109/icivpr.2017.7890883

Cited by 46 publications

(20 citation statements)

References 10 publications

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“…Thereafter, for every term the TF-IDF scores were calculated. Finally, K-means clustering technique aided in generating the document summary [16].…”

Section: Related Workmentioning

confidence: 99%

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Unsupervised Automatic Text Summarization of Konkani Texts using K-means with Elbow Method

D’Silva¹,

Sharma²

2020

International Journal of Engineering Research and Technology

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Text Summarization is an emerging field of research in Natural Language Processing (NLP). A bulk of the work is related to texts in English and other popular languages. This paper presents some of the early works attempted at performing single document extractive Automatic Text Summarization on Konkani language documents, which is an under-research language in the domain of Automatic Text Summarization (ATS). The input documents need to be cleaned of punctuation and then sentence scores are calculated for each sentence in the document. The scores for each sentence are computed using Term-Frequency/Inverse Document Frequency (TF-IDF) of constituent words and overlap with the title of the story and its positional value. K-means algorithm is applied to determine clusters of sentences for the formation of the final summary. The value of 'K' is determined using the Elbow method. The dataset employed was specially designed by the authors of the paper to perform the experiments. It consists of folk tales derived from books on Konkani literature. The performance assessment of the output summaries indicated that the summaries obtained by using three clusters were better than the ones obtained using two clusters. The proposed system exhibited promising outcome, considering, no language-dependent domain knowledge or any training corpora was utilized.

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“…Thereafter, for every term the TF-IDF scores were calculated. Finally, K-means clustering technique aided in generating the document summary [16].…”

Section: Related Workmentioning

confidence: 99%

“… TF-IDF: Also called "Term frequency", quantifies the recurrence of a word appearing in a text document [17]. TF-IDF is calculated using (1) and (2) [16],…”

Section: ) Calculating Sentence Scoresmentioning

confidence: 99%

Unsupervised Automatic Text Summarization of Konkani Texts using K-means with Elbow Method

D’Silva¹,

Sharma²

2020

International Journal of Engineering Research and Technology

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“…(Efat et al 2013) also focused on finding text features scores to summarize the document in Bengali text. (Akter et al 2017) proposed a Bengali summarizer based on K-means clustering. They clustered the document into two according to their features' scores and top scored sentences from each clusters are extracted as summary sentences.…”

Section: Bengalimentioning

confidence: 99%

Accountability of NLP Tools in Text Summarization for Indian Languages

Verma¹,

Verma²

2020

JSR

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In the era of digital world, online information is growing exponentially. It leads to emergence of inconvenient searching of relevant information in relevant time. In this regard, automatic text summarizer proves to be a good tool. It helps in creating a brief and meaningful form of the given text using natural language tool kit so that users can access the information in quick manner. Today, a lot of summarization tools are available for rich resource languages such as English. But, it seems difficult to summarize the text for Indian languages (low resource languages) due to limited availability of NLP tools and techniques for Indian languages. In this paper, we present a survey on existing text summarization methods and NLP tools for Indian languages. We also discuss about the issues associated with the Indian languages that are the bottlenecks for summarizing Indian language text.

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“…With the improved feature selection mechanism, feature extraction can be done easily. Sumya Akter et.al [5] proposed a method to extract the core content of a document. Sentence clustering approach is used for generating summary from the input document.…”

Section: Literature Reviewmentioning

confidence: 99%

Information Extraction using Tokenization And Clustering Methods

Joseph*¹,

Jeba²

2019

IJRTE

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World Wide Web has become a powerful platform for retrieval and storage of information. It is a collection of text, image and multimedia files in structured, semi structured and unstructured form. These tremendous volumes of information cannot be processed so simply. An efficient and useful algorithm is required to discover information from these data. Text mining is a method for extracting meaningful information from large volume of data. Unstructured text is easily processed by humans but it is harder for machines. Text mining task involve methods such as tokenization, feature extraction and clustering.

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An extractive text summarization technique for Bengali document(s) using K-means clustering algorithm

Cited by 46 publications

References 10 publications

Unsupervised Automatic Text Summarization of Konkani Texts using K-means with Elbow Method

Unsupervised Automatic Text Summarization of Konkani Texts using K-means with Elbow Method

Accountability of NLP Tools in Text Summarization for Indian Languages

Information Extraction using Tokenization And Clustering Methods

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