Sentiment-Aware Extractive and Abstractive Summarization for Unstructured Text Mining

2025-12-23

AI Summary

Addressing the challenge of generating sentiment-sensitive summaries from unstructured, short social media texts, this paper proposes the first sentiment-aware dual-path summarization framework. The framework integrates a TextRank-based extraction path enhanced with sentiment lexicon augmentation and fine-grained sentiment embeddings, alongside a UniLM-based generation path that jointly models emotional polarity and topical semantics. Unlike conventional summarization models—designed primarily for formal, structured documents—this approach explicitly incorporates sentiment signals to strengthen decision-support capabilities in brand monitoring and market analysis. Experimental results on user-generated content demonstrate substantial improvements: +18.7% in sentiment accuracy and +12.3% in ROUGE-L score (measuring information fidelity), while maintaining real-time processing capability.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

With the rapid growth of unstructured data from social media, reviews, and forums, text mining has become essential in Information Systems (IS) for extracting actionable insights. Summarization can condense fragmented, emotion-rich posts, but existing methods-optimized for structured news-struggle with noisy, informal content. Emotional cues are critical for IS tasks such as brand monitoring and market analysis, yet few studies integrate sentiment modeling into summarization of short user-generated texts. We propose a sentiment-aware framework extending extractive (TextRank) and abstractive (UniLM) approaches by embedding sentiment signals into ranking and generation processes. This dual design improves the capture of emotional nuances and thematic relevance, producing concise, sentiment-enriched summaries that enhance timely interventions and strategic decision-making in dynamic online environments.

Problem

• Summarizes noisy, informal user-generated texts
• Integrates sentiment modeling into text summarization
• Enhances emotional nuance capture for decision-making

Innovation

• Sentiment-aware framework for extractive and abstractive summarization
• Embeds sentiment signals into ranking and generation processes
• Produces concise, sentiment-enriched summaries for dynamic environments