Sentiment-Aware Extractive and Abstractive Summarization for Unstructured Text Mining
This work addresses the need for sentiment-aware summarization in Information Systems for tasks such as brand monitoring and market analysis, though it is incremental as it extends existing methods.
The authors tackled the problem of summarizing noisy, informal user-generated texts like social media posts by integrating sentiment modeling into both extractive and abstractive summarization methods, resulting in improved capture of emotional nuances and thematic relevance for enhanced decision-making in online environments.
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.