标题： BeliN：一种用于使用上下文特征融合生成孟加拉宗教新闻标题的新语料库 显示英文标题

标题： BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion

摘要： 自动文本摘要，特别是标题生成，对于孟加拉语宗教新闻仍然是一个关键但研究不足的领域。 现有的标题生成方法通常仅依赖文章内容，忽视了诸如情感、类别和方面等重要的上下文特征。 这一限制显著阻碍了它们的有效性和整体性能。 本研究通过引入一个新颖的语料库 BeliN（孟加拉语宗教新闻）——包括来自著名孟加拉国在线报纸的宗教新闻文章，以及 MultiGen——一种基于上下文的多输入特征融合标题生成方法，来解决这一限制。 利用基于变压器的预训练语言模型，如 BanglaT5、mBART、mT5 和 mT0，MultiGen 将额外的上下文特征——包括类别、方面和情感——与新闻内容相结合。 这种融合使模型能够捕捉传统方法常忽略的关键上下文信息。 实验结果表明，与仅使用新闻内容的基线方法相比，MultiGen 表现出优越性，BLEU 得分为 18.61，ROUGE-L 得分为 24.19，而基线方法的得分分别为 16.08 和 23.08。 这些发现强调了在低资源语言中在标题生成中融入上下文特征的重要性。 通过弥合语言和文化差距，这项研究推动了孟加拉语和其他未被充分代表语言的自然语言处理发展。 为了促进可重复性和进一步探索，数据集和实现代码可在 https://github.com/akabircs/BeliN 公开获取。 显示英文摘要

摘要： Automatic text summarization, particularly headline generation, remains a critical yet underexplored 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 underrepresented languages. To promote reproducibility and further exploration, the dataset and implementation code are publicly accessible at https://github.com/akabircs/BeliN.