BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion
Proposes BeliN corpus and MultiGen model, integrating category, aspect, sentiment features, achieving BLEU 18.61 in Bengali religious news headline generation.
Key Findings
Methodology
This study constructs BeliN, a religious news corpus with five categories, four aspects, and sentiment labels. Using transformer-based pretrained models (BanglaT5, mBART, mT5, mT0), it develops MultiGen, a multi-input feature fusion architecture that combines category, aspect, sentiment, and news content. The model employs multimodal mechanisms for deep contextual understanding, evaluated via BLEU and ROUGE, outperforming content-only baselines.
Key Results
- MultiGen achieves a BLEU score of 18.61, surpassing the baseline 16.08 by approximately 15.5%. ROUGE-L reaches 24.19, exceeding baseline 23.08, indicating improved semantic coherence.
- Incorporating category, aspect, and sentiment features significantly improves headline quality, especially in religious reports and festival news, validating the effectiveness of multimodal fusion.
- BanglaT5 demonstrates the best performance among pretrained models, with over 10% overall improvement, confirming transferability in low-resource settings.
Significance
This work advances low-resource NLP by creating a specialized religious news dataset and a multimodal fusion approach, addressing the limitations of content-only models. It enhances contextual understanding, enabling more accurate and meaningful headline generation, with broad implications for low-resource language processing and automated news summarization.
Technical Contribution
The core innovation is the MultiGen architecture, which integrates multiple auxiliary features into transformer models for headline generation. It introduces a multimodal fusion mechanism that effectively combines category, aspect, and sentiment information, leading to richer semantic representations and improved output quality. This approach extends the capabilities of pretrained transformers in low-resource, domain-specific contexts.
Novelty
This is the first work to introduce a religious news corpus with detailed annotations of category, aspect, and sentiment for Bengali, and to develop a multimodal feature fusion model tailored for low-resource language headline generation. It significantly enhances the contextual depth compared to prior content-only models.
Limitations
- The BeliN corpus is limited in size and scope, primarily sourced from Bangladeshi online news, which may limit generalizability across different regions or dialects.
- Model performance drops in highly emotional or complex contexts, indicating room for improvement in nuanced understanding.
- Multimodal fusion increases computational complexity, posing challenges for real-time deployment and resource-constrained environments.
Future Work
Future efforts will expand corpus diversity, incorporate multimodal data such as images and audio, and develop more efficient fusion mechanisms. Exploring user feedback and personalization could further refine headline relevance. Extending the approach to other low-resource languages and domains will broaden its impact.
AI Executive Summary
This research addresses the challenge of Bengali religious news headline generation, a domain with limited resources and complex contextual cues. Traditional models relying solely on news content often fail to produce engaging, accurate headlines due to the absence of auxiliary information like sentiment, category, and aspect. To overcome this, the authors developed BeliN, a comprehensive corpus containing 2520 religious news articles annotated with five categories, four aspects, and sentiment labels. Leveraging this dataset, they designed MultiGen, a multimodal feature fusion architecture built upon transformer-based pretrained models such as BanglaT5. MultiGen integrates multiple contextual features, enabling the model to grasp nuanced semantic and cultural cues. Experimental results demonstrate that MultiGen achieves a BLEU score of 18.61, significantly outperforming the baseline content-only model (16.08). ROUGE-L scores follow a similar trend, reaching 24.19 versus 23.08. These findings underscore the importance of incorporating diverse contextual information for low-resource language NLP tasks. The approach not only improves headline relevance and informativeness but also sets a new benchmark for religious news summarization in Bengali. Looking ahead, the research aims to expand the dataset, incorporate multimodal data, and optimize models for deployment in real-world applications. Overall, this work bridges linguistic and cultural gaps, advancing NLP for underrepresented languages and domains, with broad implications for automated content generation and media analysis.
Deep Analysis
Background
The evolution of NLP has seen significant progress in high-resource languages like English, with datasets such as CNN, Gigaword, and XSum supporting advanced summarization and headline generation. However, low-resource languages like Bengali lack large-scale, annotated datasets that include rich contextual features. Existing efforts, such as BNAD and Shironaam, provide some religious news data but lack detailed annotations like aspect and sentiment. Multimodal fusion techniques have gained traction in multilingual contexts but are underexplored in Bengali. Religious news, with its complex semantics and cultural nuances, demands specialized resources and models that can incorporate multiple contextual cues, which are currently scarce.
Core Problem
Current Bengali headline generation models primarily rely on news content, limiting their ability to capture the full semantic and emotional context. This results in headlines that may lack relevance, engagement, or cultural sensitivity. The scarcity of annotated religious news datasets with multiple features further hampers progress. Additionally, existing models struggle with nuanced understanding in low-resource settings, leading to suboptimal performance. Addressing these issues requires developing comprehensive datasets and models capable of multi-feature integration, especially for culturally rich domains like religious news.
Innovation
The main innovations include: 1) constructing BeliN, a religious news corpus with detailed annotations of category, aspect, and sentiment, filling a critical resource gap; 2) proposing MultiGen, a multimodal transformer-based architecture that fuses multiple auxiliary features with news content, enhancing contextual understanding; 3) demonstrating that multi-input fusion improves headline quality significantly over content-only baselines. These innovations enable more accurate, culturally aware, and contextually rich headline generation, advancing low-resource NLP and multimodal fusion techniques.
Methodology
- �� Data collection: Manually sourced 2520 religious news articles from Bangladeshi online portals, ensuring diversity.
- �� Annotation: Categorized articles into five religious groups; labeled four aspects (report, festival, education, culture); assigned sentiment (positive, negative, neutral).
- �� Dataset construction: Structured data with detailed metadata, analyzed distribution.
- �� Model design: Utilized pretrained models (BanglaT5, mBART, mT5, mT0), integrated features via a multimodal fusion layer.
- �� Training: Employed cross-entropy loss, optimized with Adam, evaluated with BLEU and ROUGE.
- �� Validation: Performed ablation studies removing features to assess contribution, compared performance across models.
Experiments
- �� Dataset split: 70% training, 15% validation, 15% testing.
- �� Baselines: Content-only transformer models.
- �� Metrics: BLEU-4, ROUGE-L.
- �� Hyperparameters: Learning rate 1e-5, batch size 16, epochs 20.
- �� Ablation: Removed individual features to analyze impact.
- �� Results: Multi-input models outperform baselines, with BLEU and ROUGE improvements confirming the effectiveness of feature fusion.
Results
- �� The proposed MultiGen model achieves BLEU 18.61, ROUGE-L 24.19, outperforming the content-only baseline by over 2 points.
- �� Incorporating category, aspect, and sentiment features leads to substantial improvements, especially in religious report and festival headlines.
- �� BanglaT5-based models show the highest performance, validating pretrained models' transferability in low-resource settings, with performance gains exceeding 10%.
Applications
- �� News agencies: Automate religious news headline creation, improve efficiency and engagement.
- �� Content curation: Enhance categorization, sentiment analysis, and personalized recommendations.
- �� Academic research: Provide a benchmark for low-resource NLP, facilitate further multimodal studies.
Limitations & Outlook
- �� Dataset size remains limited, primarily from Bangladeshi sources, which may affect generalizability.
- �� Model performance declines on highly emotional or ambiguous texts, indicating a need for more nuanced understanding.
- �� Increased computational complexity from multimodal fusion poses deployment challenges, especially in resource-constrained environments.
Plain Language Accessible to non-experts
想象你在一个厨房里做饭。以前，你只用一种调料（比如盐）来调味，虽然简单，但味道可能不够丰富。现在，你加入了多种调料：辣椒、酱油、香料等，这样做出来的菜更香、更有层次。模型就像这个厨房，结合不同的调料（新闻内容、情感、类别、角度），能做出更吸引人的标题。它用了一些聪明的技术，把这些调料融合在一起，让电脑更懂新闻的意思，就像厨师用多种调料做出美味佳肴一样。
ELI14 Explained like you're 14
想象你在学校做一个项目，你要给一篇文章起个标题。以前，你只看文章的内容，挑几个关键词就行了，但可能不够吸引人。现在，你的老师告诉你，还可以考虑文章的情感（是开心还是难过）、主题（比如节日或学习）和角度（报道或评论）。这样一来，你可以写出更贴切、更吸引人的标题。这个研究就像这样，把文章内容、情感、主题和角度结合起来，用特别的方法帮电脑理解这些信息，然后生成更好的标题。结果显示，这样做的标题比只看内容的效果更棒，能更好地吸引读者。
Abstract
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.