  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 present GT-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 by GT-D2G can
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 that GT-D2G beats other concept map generation
methods. Moreover, we specifically validate the labeling efficiency of GT-D2G
in the label-efficient learning setting and the flexibility of generated graph
sizes in controlled hyper-parameter studies.
