Evaluating Abstractive Summarization - Part 3
Conclusion
Evaluating abstractive summarization remains an open area for further improvement. Traditional metrics like ROUGE, BLEU, and BERTScore provide useful automatic evaluation but have limitations in capturing semantic similarity and nuanced aspects of summarization quality. Moreover, they require reference outputs which can be expensive to collect/label. LLM-based metrics offer promise as a reference-free method of evaluating coherence, fluency, and relevance. However, they too have potential biases favoring text generated by LLMs. Ultimately, a combination of automatic metrics and human evaluation is ideal for reliably assessing abstractive summarization systems. While human evaluation is indispensable for gaining a comprehensive understanding of summary quality, it should be complemented with automated evaluation to enable efficient, large-scale testing. The field will continue to evolve more robust evaluation techniques, balancing quality, scalability, and fairness. Advancing evaluation methods is crucial for driving progress in production applications.