Exploring Multi-Document Information Consolidation for Scientific
  Sentiment Summarization
Miao Li Jey Han Lau Eduard Hovy
Modern natural language generation systems with LLMs exhibit the capability
to generate a plausible summary of multiple documents; however, it is uncertain
if models truly possess the ability of information consolidation to generate
summaries, especially on those source documents with opinionated information.
To make scientific sentiment summarization more grounded, we hypothesize that
in peer review human meta-reviewers follow a three-layer framework of sentiment
consolidation to write meta-reviews and it represents the logic of summarizing
scientific sentiments in meta-review generation. The framework is validated via
human annotation. Based on the framework, we propose evaluation metrics to
assess the quality of generated meta-reviews, and we find that the hypothesis
of the sentiment consolidation framework works out empirically when we
incorporate it as prompts for LLMs to generate meta-reviews in extensive
experiments.