Zero-shot Conversational Summarization Evaluations with small Large Language Models

• URL: http://arxiv.org/abs/2311.18041v1
• Date: Wed, 29 Nov 2023 19:34:34 GMT
• Title: Zero-shot Conversational Summarization Evaluations with small Large Language Models
• Authors: Ramesh Manuvinakurike, Saurav Sahay, Sangeeta Manepalli, Lama Nachman
• Abstract summary: Large Language Models (LLMs) exhibit powerful summarization abilities. We evaluate LLMs on conversational summarization and showcase their performance on various prompts. We also evaluate the models with human evaluations and discuss the limitations of the models on conversational summarization.
• Score: 7.525771026977357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) exhibit powerful summarization abilities. However, their capabilities on conversational summarization remains under explored. In this work we evaluate LLMs (approx. 10 billion parameters) on conversational summarization and showcase their performance on various prompts. We show that the summaries generated by models depend on the instructions and the performance of LLMs vary with different instructions sometimes resulting steep drop in ROUGE scores if prompts are not selected carefully. We also evaluate the models with human evaluations and discuss the limitations of the models on conversational summarization

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