Advancing Hindi Text Summarization: Named Entity Recognition and Content Augmentation Strategies

We explore advancements in Hindi text summarization, a critical area in natural language processing that aids in managing information overload. Despite a growing corpus of Hindi data, there’s a significant gap in practical summarization tools due to intricate linguistic features and limited resources compared to English. Previous works focused on extractive methods, but recent shifts towards abstractive approaches promise more natural and coherent summaries by understanding and paraphrasing content. Our research introduces novel methodologies, Named Entity Aware-Abstractive Text Summarization (NEA-ATS) and Query-Driven Content Augmentation for Summarization (QDCAS), aimed at enhancing the accuracy and richness of Hindi summaries. NEA-ATS integrates Named Entity Recognition to prioritize crucial information, improving language model attention to critical details but occasionally disrupting context. While NEA-ATS shows some improvements, it occasionally disrupts the text’s context, leading to only marginal gains in summary quality. Meanwhile, QDCAS addresses extrinsic hallucinations—common in state-of-the-art models—by augmenting source documents with relevant content through focused web crawling—a technique to selectively gather topic-specific web pages—broadening contextual understanding and refining outputs. Empirical results demonstrate the effectiveness of QDCAS, showing marginal improvements in ROUGE and BERTScores over traditional language models. This work advances Hindi text summarization and explores content-rich strategies, potentially expanding to other languages and domains.