Context-Aware Summarization of Social Media Chat: Techniques, Challenges, and Future Directions with Large Language Models

I. Introduction to Chat Summarization The proliferation of digital communication has led to an exponential increase in conversational data generated through messaging applications, social media platforms, online forums, and collaborative tools. Effectively managing and extracting value from this vast amount of information necessitates automated solutions, among which dialogue summarization plays a critical role. Dialogue summarization is formally defined as the task of condensing conversations involving two or more participants into shorter, informative versions that capture the most salient information.1 The need for such condensation is driven by the sheer volume of dialogue data, making manual review impractical in many scenarios.3 Chat summarization emerges as a specialized sub-field within dialogue summarization, focusing specifically on the characteristics of informal, text-based, multi-turn conversations commonly found on social media and messaging platforms.4 These chats often exhibit distinct linguistic features, including brevity, non-standard grammar, slang, abbreviations, and the prevalent use of emojis or emoticons.7 The applications of dialogue and chat summarization are diverse, ranging from generating meeting minutes and summarizing customer service interactions to recapping doctor-patient consultations and digesting sprawling social media discussions.1