Official repo for paper Less is More for Long Document Summary Evaluation by LLMs .

@inproceedings{wu2024long,
      title={{L}ess is {M}ore for {L}ong {D}ocument {S}ummary {E}valuation by {LLM}s}, 
      author={Yunshu Wu\textsuperscript{*} and Hayate Iso\textsuperscript{*} and Pouya Pezeshkpour and Nikita Bhutani and Estevam Hruschka},
      booktitle={Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics},
      year={2024},
      note={\textsuperscript{*}These authors contributed equally to this work.},
}

Recent research indicates that Large Language Models (LLMs) can effectively evaluate Natural Language Generation (NLG), but their suitability for evaluating long documents is uncertain due to increased costs and potential quality issues. In this work, we introduce a cost-effective approach that utilizes extraction techniques to create condensed, influential documents, followed by LLMs to evaluate summary quality.

Here we use G-eval as our baseline.

To run G-eval on all benchmarks, use the shell, remember to change the --key into your openai-key:

./benchmark_run.sh 

For arXiv and GovReport, --dimension should be either relevance or factual_consistency, and for PubMed and SQuALITY, --dimension should only be faithfulness.

Our four extraction methods:

• LEAD: Extract the first N tokens from the source document (consisting of complete sentences).
• ROUGE: Extract sentences from the source document that maximize recall of ROUGE score with the model-generated summary until it reaches N tokens.
• BERTScore: Extract sentences as in ROUGE, but use the recall of BERTScore as the criteria.
• NLI: Extract sentences that are entailed or contradicted by each sentence in the summary as premises using NLI models until it reaches N tokens. This process aims to extract sentences that are semantically relevant to the summary being evaluated.

Our evaluation method is cheap and of good quality:

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