Dialog summarization has become increasingly important in managing and comprehending large-scale conversations across various domains. This task presents unique challenges in capturing the key points, context, and nuances of multi-turn long conversations for summarization. It is worth noting that the summarization techniques may vary based on specific requirements such as in a shopping-chatbot scenario, the dialog summary helps to learn user preferences, whereas in the case of a customer call center, the summary may involve the problem attributes that a user specified, and the final resolution provided. This work emphasizes the significance of creating coherent and contextually rich summaries for effective communication in various applications. We explore current state-of-the-art approaches for long dialog summarization in different domains and benchmark metrics based evaluations show that one single model does not perform well across various areas for distinct summarization tasks.
@article{arxiv.2402.16986,
title = {Long Dialog Summarization: An Analysis},
author = {Ankan Mullick and Ayan Kumar Bhowmick and Raghav R and Ravi Kokku and Prasenjit Dey and Pawan Goyal and Niloy Ganguly},
journal= {arXiv preprint arXiv:2402.16986},
year = {2024}
}