学生のNguyen, Tien Minhさんと知能ロボティクス領域のNguyen, Minh Le准教授らがSoICT 2017においてBest Paper Awardを受賞
学生のNguyen, Tien Minhさん（博士後期課程3年、知能ロボティクス領域・グェン研究室）と知能ロボティクス領域のNguyen, Minh Le准教授らがSoICT 2017においてBest Paper Awardを受賞しました。
SoICT (International Symposium on Information and Communication Technology) is an international conference focusing on Information and Communication Technology. This was the eighth and held in Nha Trang, Vietnam. 145 papers were submitted from all over the world (22 countries), and 65 papers were accepted this year. High-quality papers were nominated by program committee members' voting. Finally, one paper was awarded after hearing the presentation.
Minh-Tien Nguyen, Viet Tran Cuong, Xuan Hoai Nguyen, and Minh-Le Nguyen
Utilizing User Posts to Enrich Web Document Summarization with Matrix Co-factorization.
In the context of social media, users tend to post relevant information corresponding to an event mentioned in a Web document. This paper presents a model to capture the nature of the relationships between sentences and user posts such as relevant comments in sharing hidden topics for enriching summarization. Unlike the previous methods which usually base on hand-crafted features, our approach ranks sentences and comments based on their importance affecting the topics. The sentence-comment relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our newly proposed matrix co-factorization algorithm computes the score of each sentence and comment and extracts top m ranked sentences and m comments as the summarization. Experimental results on two datasets in English and Vietnamese of the social context summarization task and DUC 2004 confirm the efficiency of our model in summarizing Web documents.
This is my great honor to receive the Best Paper Award of SoICT 2017. I would like to thank my supervisor - Associate Professor Minh Le Nguyen for supervising me during my research. I would like to thank my co-authors (Prof Hoai and Mr. Cuong) for their comments and discussions to adapt matrix factorization to this task. I am also really thankful to JAIST and other members in Nguyen Lab to support me to receive this award.