Year: 2023
Type: article
Abstract: Recent years have witnessed the rapid development of concept map generation techniques due to their advantages in providing well-structured summarization of knowledge from free texts. Traditional unsupervised methods do not generate task-oriented concept maps, whereas deep generative models require large amounts of training data. In this work, we presentGT-D2G(Graph Translation-based Document To Graph), an automatic concept map generation framework that leverages generalized NLP pipelines to derive semantic-rich initial graphs, and translates them into more concise structures under the weak supervision of downstream task labels. The concept maps generated byGT-D2Gcan provide interpretable summarization of structured knowledge for the input texts, which are demonstrated through human evaluation and case studies on three real-world corpora. Further experiments on the downstream task of document classification show thatGT-D2Gbeats other concept map generation methods. Moreover, we specifically validate the labeling efficiency ofGT-D2Gin the label-efficient learning setting and the flexibility of generated graph sizes in controlled hyper-parameter studies.
Authors: Jiaying Lu, Xiangjue Dong, Carl Yang
Institutions: Emory University, Texas A&M University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4323338317