Summarizing C hinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention (2020)

Abstract: Online search engines are a popular source of medical information for users, where users can enter questions and obtain relevant answers. It is desirable to generate answer summaries for online search engines, particularly summaries that can reveal direct answers to questions. Moreover, answer summaries are expected to reveal the most relevant information in response to questions; hence, the summaries should be generated with a focus on the question, which is a challenging topic-focused summarization task. In this paper, we propose an approach that utilizes graph convolution networks and question-focused dual attention for Chinese medical answer summarization. We first organize the original long answer text into a medical concept graph with graph convolution networks to better understand the internal structure of the text and the correlation between medical concepts. Then, we introduce a question-focused dual attention mechanism to generate summaries relevant to questions. Experimental results demonstrate that the proposed model can generate more coherent and informative summaries compared with baseline models.

## introduction online search engines (e.g., google, bing) have a wealth of fresh health-related information, which is appealing for users with medical questions. users can enter questions to obtain relevant answers. however, most answers generated by domain experts are incredibly long, and some are even more than 512 words. it is intuitive to generate answer summaries, which will benefit both users and search engines. such abstract resources are valuable to attract users' attention and encourage clicking and reading. moreover, answer summaries are expected to reveal the most relevant information in although the answer summarization task is not new, studies and corpus for the chinese medical domain are still limited. to this end, we propose a graph convolution network with question-focused dual attention (q-gcn) model to generate summaries. our motivation is that graph-based structure can better represent the correlation between diverse concepts in the answer and capture the plot of the whole text. specifically, we decompose the long answer text into several entities/keywords centered clusters of texts and represent the answer with a medical concept graph. each vertex of the graph is formed with concept clusters regarding the entities/keywords. we calculate the edge between vertices via semantic relations between the vertices. moreover, to enhance the relevance of the summaries regarding questions, we propose a question-focused dual attention mechanism to extract the primary information from the answer. we highlight our contributions as follows: ## experiments we conduct three kinds of experiments: 1) automatic and manual evaluation with ablation study for chinese medical answer summarization; 2) further experiments on wikihowqa; 3) model analysis regarding question length, question-focused dual attention, and error analysis. ## conclusion and future work in this paper, we propose an approach of graph convolution network with question-focused dual attention to generate chinese answer summaries. experimental results indicate that our model can summarize more coherently and informatively, thereby showing that organizing long text with a graph structure is beneficial and question-focused dual attention further improves the informativeness and correlation. in the future, we plan to 1) exploit knowledge such as commonsense to generate logical summaries; 2) investigate efficient methodologies to model the correlation between concepts;