Long Dialog Summarization: An Analysis
Ankan Mullick Ayan Kumar Bhowmick Raghav R Ravi Kokku Prasenjit Dey Pawan Goyal Niloy Ganguly
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.