LDKP - A Dataset for Identifying Keyphrases from Long Scientific Documents
Published in DL4SR’22: Workshop on Deep Learning for Search and Recommendation, co-located with the 31st ACM International Conference on Information and Knowledge Management (CIKM), 2022
Identifying keyphrases (KPs) from text documents is a fundamental task in natural language processing and information retrieval. Vast majority of the benchmark datasets for this task are from the scientific domain containing only the document title and abstract information. This limits keyphrase extraction (KPE) and keyphrase generation (KPG) algorithms to identify keyphrases from human-written summaries that are often very short (≈ 8 sentences). This presents three challenges for real-world applications: i) human-written summaries are unavailable for most documents, ii) a vast majority of the documents are long, and iii) a high percentage of KPs are directly found beyond the limited context of the title and the abstract. Therefore, we release two extensive corpora mapping KPs of ≈ 1.3𝑀 and ≈ 100𝐾 scientific articles with their fully extracted text and additional metadata including publication venue, year, author, field of study, and citations for facilitating research on this real-world problem. Additionally, we also benchmark and report the performances of different unsupervised as well as supervised algorithms for keyphrase extraction on long scientific documents. Our experiments show that formulating keyphrase extraction as a sequence tagging task with modern transformer language models capable of processing long text sequences such as longformer has advantages over the traditional algorithms, not only resulting in better performances in terms of F1 metrics but also in learning to extract optimal number of keyphrases from the input documents.