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Radiology reports play a critical role in communicating medical findings tophysicians. In each report, the impression section summarizes essentialradiology findings. In clinical practice, writing impression is highly demandedyet time-consuming and prone to errors for radiologists. Therefore, automaticimpression generation has emerged as an attractive research direction tofacilitate such clinical practice. Existing studies mainly focused onintroducing salient word information to the general text summarizationframework to guide the selection of the key content in radiology findings.However, for this task, a model needs not only capture the important words infindings but also accurately describe their relations so as to generatehigh-quality impressions. In this paper, we propose a novel method forautomatic impression generation, where a word graph is constructed from thefindings to record the critical words and their relations, then a Word Graphguided Summarization model (WGSum) is designed to generate impressions with thehelp of the word graph. Experimental results on two datasets, OpenI andMIMIC-CXR, confirm the validity and effectiveness of our proposed approach,where the state-of-the-art results are achieved on both datasets. Furtherexperiments are also conducted to analyze the impact of different graph designsto the performance of our method.

Word Graph Guided Summarization for Radiology Findings