Rethinking Scientific Summarization Evaluation: Grounding Explainable Metrics on Facet-aware Benchmark
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View abstract on PubMed
Summary
This summary is machine-generated.Evaluating scientific text summarization is challenging for current models. A new Facet-aware Metric (FM) and dataset improve assessment, showing fine-tuned models can rival large language models (LLMs).
Area Of Science
- Natural Language Processing
- Scientific Communication
- Artificial Intelligence
Background
- Large language models (LLMs) excel at general text summarization but struggle with scientific corpora due to complex language and specialized knowledge.
- Traditional summarization evaluation metrics (e.g., n-gram, embedding comparison, QA) are inadequate for scientific content, failing to capture conceptual understanding or key information.
- There is a lack of standardized benchmarks for evaluating scientific summarization.
Purpose Of The Study
- To analyze the effectiveness of LLMs in scientific text summarization.
- To introduce a novel evaluation metric, the Facet-aware Metric (FM), for scientific summaries.
- To create a new dataset, the Facet-based scientific summarization Dataset (FD), for benchmark evaluation.
Main Methods
- Conceptual and experimental analysis of scientific summarization.
- Development of the Facet-aware Metric (FM) using LLMs for advanced semantic matching across different facets.
- Curation of the Facet-based scientific summarization Dataset (FD) with facet-level annotations.
Main Results
- The Facet-aware Metric (FM) provides a more logical and thorough evaluation of scientific abstracts compared to traditional methods.
- Fine-tuned smaller models demonstrate competitive performance against LLMs in scientific summarization tasks.
- LLMs exhibit limitations in effectively learning from in-context information within scientific domains.
Conclusions
- The Facet-aware Metric (FM) offers a superior approach to evaluating scientific summaries.
- Smaller, fine-tuned models show promise for scientific summarization, challenging the dominance of large models.
- Future enhancements for LLMs should focus on improving their ability to learn from in-context scientific information.
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