Eval_Summarization

[A python package] (https://github.com/Yale-LILY/SummEval)

1. Kryściński, Wojciech, et al. “Neural text summarization: A critical evaluation.” .
2. Zhang, Tianyi, et al. “Benchmarking large language models for news summarization.”. code
3. Kryściński, Wojciech, et al. “Evaluating the factual consistency of abstractive text summarization.” code
4. Pagnoni, Artidoro, Vidhisha Balachandran, and Yulia Tsvetkov. “Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics.”. code
5. Maynez, Joshua, et al. “On faithfulness and factuality in abstractive summarization.” .
6. Lin, Chin-Yew. “Rouge: A package for automatic evaluation of summaries.”.
7. Banerjee, Satanjeev, and Alon Lavie. “METEOR: An automatic metric for MT evaluation with improved correlation with human judgments.”
8. Zhang, Tianyi, et al. “Bertscore: Evaluating text generation with bert.” code
9. Zhao, Wei, et al. “MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance.” code
10. Fabbri, Alexander R., et al. “Summeval: Re-evaluating summarization evaluation.”
11. He, Tingting, et al. “ROUGE-C: A fully automated evaluation method for multi-document summarization.”
12. Liu, Yang, et al. “Gpteval: NLG evaluation using gpt-4 with better human alignment.”
13. Laban, Philippe, et al. “SummaC: Re-visiting NLI-based models for inconsistency detection in summarization.”
14. Gekhman, Zorik, et al. “Trueteacher: Learning factual consistency evaluation with large language models.”
15. Scialom, Thomas, et al. “Answers unite! unsupervised metrics for reinforced summarization models.”
16. Durmus, Esin, He He, and Mona Diab. “FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization.”
17. Scialom, Thomas, et al. “Questeval: Summarization asks for fact-based evaluation.”
18. Fabbri, Alexander R., et al. “QAFactEval: Improved QA-based factual consistency evaluation for summarization.”
19. Böhm, Florian, et al. “Better rewards yield better summaries: Learning to summarise without references.”
20. Stiennon, Nisan, et al. “Learning to summarize with human feedback.”
21. Wu, Jeff, et al. “Recursively summarizing books with human feedback.”
22. Manakul, Potsawee, Adian Liusie, and Mark JF Gales. “Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models.”