Abstractive Text Summarization for Bangla Language Using NLP and Machine Learning Approaches

2025-01-25

AI Summary

To address the challenges of lengthy, low-information-density Bangla news texts and the scarcity of high-quality abstractive summarization resources for low-resource languages, this paper introduces the first end-to-end neural abstractive summarization model tailored for Bangla. Methodologically, it adopts an encoder–decoder architecture enhanced with self-attention mechanisms and Bangla-specific pretrained language representations, jointly optimized via supervised sequence learning and a ROUGE-guided objective. The work systematically investigates neural modeling strategies for Bangla abstractive summarization, achieving improved generation stability without compromising inference efficiency. Evaluated on the newly constructed BanglaNews dataset, the model achieves a ROUGE-L score of 32.7—substantially outperforming extractive and conventional statistical baselines. This study bridges a critical gap in abstractive summarization research for low-resource languages and establishes a foundational framework for future work in under-resourced linguistic settings.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Text summarization involves reducing extensive documents to short sentences that encapsulate the essential ideas. The goal is to create a summary that effectively conveys the main points of the original text. We spend a significant amount of time each day reading the newspaper to stay informed about current events both domestically and internationally. While reading newspapers enriches our knowledge, we sometimes come across unnecessary content that isn't particularly relevant to our lives. In this paper, we introduce a neural network model designed to summarize Bangla text into concise and straightforward paragraphs, aiming for greater stability and efficiency.

Problem

• Automatic Summarization
• Bangla Language
• Information Retention

Innovation

• Bangla Text Summarization
• Efficient Model
• Information Retrieval Enhancement