@inproceedings{patel-caragea-2021-exploiting,
    title = "Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers",
    author = "Patel, Krutarth  and
      Caragea, Cornelia",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.eacl-main.136/",
    doi = "10.18653/v1/2021.eacl-main.136",
    pages = "1585--1591",
    abstract = "Keyphrases associated with research papers provide an effective way to find useful information in the large and growing scholarly digital collections. In this paper, we present KPRank, an unsupervised graph-based algorithm for keyphrase extraction that exploits both positional information and contextual word embeddings into a biased PageRank. Our experimental results on five benchmark datasets show that KPRank that uses contextual word embeddings with additional position signal outperforms previous approaches and strong baselines for this task."
}