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VIDEO DOI: https://doi.org/10.48448/w5nw-0751

poster

ACL 2024

August 13, 2024

Bangkok, Thailand

A Sentiment Consolidation Framework for Meta-Review Generation

keywords:

meta-review generation

large language models

multi-document summarization

Modern natural language generation systems with Large Language Models (LLMs) exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain if they truly possess the capability of information consolidation to generate summaries, especially on documents with opinionated information. We focus on meta-review generation, a form of sentiment summarisation for the scientific domain. To make scientific sentiment summarization more grounded, we hypothesize that human meta-reviewers follow a three-layer framework of sentiment consolidation to write meta-reviews. Based on the framework, we propose novel prompting methods for LLMs to generate meta-reviews and evaluation metrics to assess the quality of generated meta-reviews. Our framework is validated empirically as we find that prompting LLMs based on the framework --- compared with prompting them with simple instructions --- generates better meta-reviews.

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SlidesTranscript English (automatic)

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