Approaches and Trends of Automatic Bangla Text Summarization

Haque, Md. Majharul; Pervin, Suraiya; Hossain, Anowar; Begum, Zareena

doi:10.4018/ijtd.20201001.oa

Cited by 10 publications

(5 citation statements)

References 30 publications

(88 reference statements)

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“…The RMSE and correlation scores indicate the degree to which the two mentioned social networks coincide, with a very low RMSE score and a significant correlation (p-value) suggesting that the constructed social network is very close to the ideal ground truth network. This has implications as a novel method for a text summarization procedure and as an alternative to the contemporary techniques [56][57][58]. This is particularly significant for the text summarization of fiction, which depends greatly on elements such as characters and social relations [59].…”

Section: Discussionmentioning

confidence: 99%

A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata

Gultepe,

Mathangi

2023

Heritage

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Despite the advances in computational literary analysis of Western literature, in-depth analysis of the South Asian literature has been lacking. Thus, social network analysis of the main characters in the Indian epic Mahabharata was performed, in which it was prepossessed into verses, followed by a term frequency–inverse document frequency (TF-IDF) transformation. Then, Latent Semantic Analysis (LSA) word vectors were obtained by applying compact Singular Value Decomposition (SVD) on the term–document matrix. As a novel innovation to this study, these word vectors were adaptively converted into a fully connected similarity matrix and transformed, using a novel locally weighted K-Nearest Neighbors (KNN) algorithm, into a social network. The viability of the social networks was assessed by their ability to (i) recover individual character-to-character relationships; (ii) embed the overall network structure (verified with centrality measures and correlations); and (iii) detect communities of the Pandavas (protagonist) and Kauravas (antagonist) using spectral clustering. Thus, the proposed scheme successfully (i) predicted the character-to-character connections of the most important and second most important characters at an F-score of 0.812 and 0.785, respectively, (ii) recovered the overall structure of the ground-truth networks by matching the original centralities (corr. > 0.5, p < 0.05), and (iii) differentiated the Pandavas from the Kauravas with an F-score of 0.749.

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Section: Discussionmentioning

confidence: 99%

A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata

Gultepe,

Mathangi

2023

Heritage

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“…Bangla text data preprocessing's most common challenges are character-set dependence, corpus dependence, applica-tion dependence, writing system dependence, tokenizing of punctuations, tokenizing of space-delimiters, preprocessing of Bangla grammatically wrong words, preprocessing of ambiguous words, preprocessing of erroneous words, preprocessing multi-label Bangla data. Moreover, the automatic computerized services for Bangla text preprocessing and relevant research works are hardly available for facilitating researches on Bangla language text data [81], [191].…”

Section: Current Challenges and Future Trends In Bnlp A Challenges In...mentioning

confidence: 99%

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Sen,

Fuad,

Islam

et al. 2021

Preprint

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The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories,

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“…We found that Yeasmin et al [1] have described the different techniques regarding abstractive approaches. Then as we decided to focus on abstractive text summarization approaches on the Bengali language context, we covered Haque et al [2] where 14 approaches of Bengali text summarization regarding both extractive and abstractive approaches are described. In 2004, Islam et al [3] first introduced Bengali extractive summarization based on document indexing and keyword-based information retrieval.…”

Section: Related Workmentioning

confidence: 99%

Bengali Abstractive News Summarization (BANS): A Neural Attention Approach

Bhattacharjee

Avi

Islam

et al. 2020

Advances in Intelligent Systems and Computing

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ive summarization is the process of generating novel sentences based on the information extracted from the original text document while retaining the context. Due to abstractive summarization's underlying complexities, most of the past research work has been done on the extractive summarization approach. Nevertheless, with the triumph of the sequence-to-sequence (seq2seq) model, abstractive summarization becomes more viable. Although a significant number of notable research has been done in the English language based on abstractive summarization, only a couple of works have been done on Bengali abstractive news summarization (BANS). In this article, we presented a seq2seq based Long Short-Term Memory (LSTM) network model with attention at encoder-decoder. Our proposed system deploys a local attention-based model that produces a long sequence of words with lucid and human-like generated sentences with noteworthy information of the original document. We also prepared a dataset of more than 19k articles and corresponding human-written summaries collected from bangla.bdnews24.com 1 which is till now the most extensive dataset for Bengali news document summarization and publicly published in Kaggle 2 . We evaluated our model qualitatively and quantitatively and compared it with other published results. It showed significant improvement in terms of human evaluation scores with state-of-the-art approaches for BANS.

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Approaches and Trends of Automatic Bangla Text Summarization

Cited by 10 publications

References 30 publications

A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata

A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata

Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods

Bengali Abstractive News Summarization (BANS): A Neural Attention Approach

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