Systematic TextRank Optimization in Extractive Summarization
Morris Zieve, Anthony Gregor, Frederik Juul Stokbaek, Hunter Lewis, Ellis Marie Mendoza, Benyamin Ahmadnia
Abstract
With the ever-growing amount of textual data, extractive summarization has become increasingly crucial for efficiently processing information. The TextRank algorithm, a popular unsupervised method, offers excellent potential for this task. In this paper, we aim to optimize the performance of TextRank by systematically exploring and verifying the best preprocessing and fine-tuning techniques. We extensively evaluate text preprocessing methods, such as tokenization, stemming, and stopword removal, to identify the most effective combination with TextRank. Additionally, we examine fine-tuning strategies, including parameter optimization and incorporation of domain-specific knowledge, to achieve superior summarization quality.- Anthology ID:
- 2023.ranlp-1.135
- Volume:
- Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 1274–1281
- Language:
- URL:
- https://aclanthology.org/2023.ranlp-1.135
- DOI:
- Cite (ACL):
- Morris Zieve, Anthony Gregor, Frederik Juul Stokbaek, Hunter Lewis, Ellis Marie Mendoza, and Benyamin Ahmadnia. 2023. Systematic TextRank Optimization in Extractive Summarization. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 1274–1281, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Systematic TextRank Optimization in Extractive Summarization (Zieve et al., RANLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2023.ranlp-1.135.pdf