Long Dialog Summarization: An Analysis
<|reference_start|>Long Dialog Summarization: An Analysis: 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.<|reference_end|>
@article{mullick2024long,
    title={Long Dialog Summarization: An Analysis},
    author={Ankan Mullick, Ayan Kumar Bhowmick, Raghav R, Ravi Kokku, Prasenjit
  Dey, Pawan Goyal, Niloy Ganguly},
    journal={arXiv preprint arXiv:2402.16986},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16986},
    primaryClass={cs.CL cs.IR}
}