BeliN: A novel corpus for Bengali religious news headline generation using contextual feature fusion

Md Osama , Ashim Dey , Kawsar Ahmed , Muhammad Ashad Kabir
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Abstract

Automatic text summarization, particularly headline generation, remains a critical yet under-explored area for Bengali religious news. Existing approaches to headline generation typically rely solely on the article content, overlooking crucial contextual features such as sentiment, category, and aspect. This limitation significantly hinders their effectiveness and overall performance. This study addresses this limitation by introducing a novel corpus, BeliN (Bengali Religious News) – comprising religious news articles from prominent Bangladeshi online newspapers, and MultiGen – a contextual multi-input feature fusion headline generation approach. Leveraging transformer-based pre-trained language models such as BanglaT5, mBART, mT5, and mT0, MultiGen integrates additional contextual features – including category, aspect, and sentiment – with the news content. This fusion enables the model to capture critical contextual information often overlooked by traditional methods. Experimental results demonstrate the superiority of MultiGen over the baseline approach that uses only news content, achieving a BLEU score of 18.61 and ROUGE-L score of 24.19, compared to baseline approach scores of 16.08 and 23.08, respectively. These findings underscore the importance of incorporating contextual features in headline generation for low-resource languages. By bridging linguistic and cultural gaps, this research advances natural language processing for Bengali and other under-represented languages. To promote reproducibility and further exploration, the dataset and implementation code are publicly accessible at https://github.com/akabircs/BeliN.
基于上下文特征融合的孟加拉语宗教新闻标题生成新语料库
自动文本摘要,特别是标题生成,仍然是孟加拉宗教新闻的一个关键但尚未开发的领域。现有的标题生成方法通常只依赖于文章内容,忽略了关键的上下文特征,如情感、类别和方面。这一限制极大地阻碍了它们的有效性和整体性能。本研究通过引入一个新的语料库——BeliN(孟加拉宗教新闻)——包括来自孟加拉国著名在线报纸的宗教新闻文章,以及MultiGen——一种上下文多输入特征融合标题生成方法,解决了这一限制。利用基于转换器的预训练语言模型,如BanglaT5、mBART、mT5和mT0, MultiGen将额外的上下文特性(包括类别、方面和情感)集成到新闻内容中。这种融合使模型能够捕获通常被传统方法忽略的关键上下文信息。实验结果表明,MultiGen优于仅使用新闻内容的基线方法,BLEU得分为18.61,ROUGE-L得分为24.19,而基线方法得分分别为16.08和23.08。这些发现强调了在低资源语言的标题生成中纳入上下文特征的重要性。通过弥合语言和文化差距,本研究促进了孟加拉语和其他代表性不足的语言的自然语言处理。为了促进再现性和进一步的探索,数据集和实现代码可在https://github.com/akabircs/BeliN上公开访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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