Journal of Data and Information Science ›› 2021, Vol. 6 ›› Issue (3): 35-57.doi: 10.2478/jdis-2021-0013

• Research Papers • Previous Articles     Next Articles

Automatic Keyphrase Extraction from Scientific Chinese Medical Abstracts Based on Character-Level Sequence Labeling

Liangping Ding1,2, Zhixiong Zhang1,2,3,(), Huan Liu1,2, Jie Li1,2, Gaihong Yu1,2   

  1. 1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
    2University of Chinese academy of sciences, Beijing 100049, China
    3Wuhan Library, Chinese Academy of Sciences, Wuhan 430071, China
  • Received:2020-10-31 Revised:2020-12-27 Accepted:2021-01-15 Online:2021-08-20 Published:2021-07-22
  • Contact: Zhixiong Zhang E-mail:zhangzhx@mail.las.ac.cn

Abstract:

Purpose: Automatic keyphrase extraction (AKE) is an important task for grasping the main points of the text. In this paper, we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research.

Design/methodology/approach: We regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT, which was released by Google in 2018. We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain, which contains 100,000 abstracts as training set, 6,000 abstracts as development set and 3,094 abstracts as test set. We use unsupervised keyphrase extraction methods including term frequency (TF), TF-IDF, TextRank and supervised machine learning methods including Conditional Random Field (CRF), Bidirectional Long Short Term Memory Network (BiLSTM), and BiLSTM-CRF as baselines. Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models.

Findings: Compared with character-level BiLSTM-CRF, the best baseline model with F1 score of 50.16%, our character-level sequence labeling model based on BERT obtains F1 score of 59.80%, getting 9.64% absolute improvement.

Research limitations: We just consider automatic keyphrase extraction task rather than keyphrase generation task, so only keyphrases that are occurred in the given text can be extracted. In addition, our proposed dataset is not suitable for dealing with nested keyphrases.

Practical implications: We make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts (CAKE) publicly available for the benefits of research community, which is available at:https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction.

Originality/value: By designing comparative experiments, our study demonstrates that character-level formulation is more suitable for Chinese automatic keyphrase extraction task under the general trend of pretrained language models. And our proposed dataset provides a unified method for model evaluation and can promote the development of Chinese automatic keyphrase extraction to some extent.

Key words: Automatic keyphrase extraction, Character-level sequence labeling, Pretrained language model, Scientific chinese medical abstracts