A Survey of Keyphrase Generation
Authors: Florian Boudin, Akiko Aizawa


Reviewer paper summaries: This paper surveys recent advances in keyphrase generation, analyzing over 40 related papers, revealing the limitations of existing methods, and proposing future research directions. The paper not only summarizes the current state of research but also provides a strong PLM-based keyphrase generation model and an evaluation framework to facilitate future research.
Summary of strengths: 1. The article covers over 40 papers, providing a thorough overview of the keyphrase generation field.
2. It offers a detailed analysis of existing datasets and evaluation metrics, highlighting similarities between datasets and issues in evaluation methods.
3. It includes a trained PLM model and an evaluation framework, enhancing reproducibility and further research.
4.  The article identifies current research gaps and proposes specific improvements and future research directions.
5. The paper categorizes and evaluates existing research from multiple perspectives, including model architectures, data resources, and research methodologies.
Summary of weaknesses: 1. Although multiple datasets are analyzed, the focus is primarily on scientific abstracts, with limited coverage of datasets from other domains.
2. Despite providing a model and evaluation framework, the paper lacks details on data preprocessing and model training specifics, which may affect reproducibility.
3. While it reviews existing methods, the article does not propose new algorithms or techniques, focusing more on summarizing and evaluating current research.
4. Some analyses, such as model performance comparisons, might lack deeper experiments and data support.
5. The selection of papers is primarily from the ACL Anthology and a few other sources, which might introduce sample bias and not fully represent the entire field.
6. This paper lacks a detailed analysis of keyphrase generation based on LLMs.
7. I think we should summarize the existing keyphrase generation work related to LLMs (whatever simply using zero-shot or few-shot prompts), and with the help of non-LLMs related work, put forward some views and insights on the future of keyphrase generation methods based on LLMs, rather than always focusing on traditional models and do a survey work around traditional models, which some existing papers (e.g., Wu et al. 2023) have already studied very thoroughly.
8. The paper lacks some in-depth analysis and interesting insights. I would like to see what inspiration this review can bring to later scholars rather than simply summarizing existing work.
Comments, suggestions and typos: see Weaknesses
Overall assessment scores: 1