Keyword Extraction in Scientific Documents
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Abstract (arXiv)
The scientific publication output grows exponentially. Therefore, it is increasingly challenging to keep track of trends and changes. Understanding scientific documents is an important step in downstream tasks such as knowledge graph building, text mining, and discipline classification. In this workshop, we provide a better understanding of keyword and keyphrase extraction from the abstract of scientific publications.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 70 references that are in the map. First posted on arXiv 5 Jul 2022.
Builds on
14 of 70 references.
References · 14
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
- 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
- 2017HDLTex: Hierarchical Deep Learning for Text Classificationcited by 105
- 2014Growth rates of modern science: A bibliometric analysis based on the number of publications and cited referencescited by 63
- 2018Unsupervised Keyphrase Extraction with Multipartite Graphscited by 50
- 2016Variations of the Similarity Function of TextRank for Automated Summarizationcited by 27
- 2020HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognitioncited by 26
- 2022Representation Learning for Resource-Constrained Keyphrase Generationcited by 14
- 2018Theme-weighted Ranking of Keywords from Text Documents using Phrase Embeddingscited by 5
- 2014Unsupervised Keyword Extraction from Polish Legal Textscited by 2
2 more
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Citing papers per year: 2023: 3.
3 papers.