自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

                  A Selectable and Interactive Model for Abstractive Summarization

                  NLPIR SEMINAR Y2019#4

                  INTRO?

                         In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.

                  Arrangement

                  This week’s seminar is organized as follows:

                  1. The seminar time is 1.pm, Mon, at Zhongguancun Technology Park ,Building 5, 1306.
                  2. The lecturer is Xi Zhang, the paper’s title is A Selectable and Interactive Model for Abstractive Summarization.
                  3. The seminar will be hosted by Qinghong Jiang.
                  4. Attachment is the paper of this seminar, please download in advance.

                  Anyone interested in this topic is welcomed to join us.
                  the following is the abstract for this week’s paper.

                  A Selectable and Interactive Model for Abstractive Summarization

                  Xi Zhang

                  Abstract

                         Sequence-to-sequence neural networks with attention have been widely used in text summarization as the amount of textual data has exploded in recent years. The traditional approach to automatic sum-marization is based only on word attention and most of them focus on generating a single sentence summarization. In this work, we propose a novel model with a dual attention that considers both sentence and word information and then generates a multi-sentence summarization word by word. Additionally, we enhance our model with a copy-generator network to solve the out-of-vocabulary (OOV) problem. The model shows signif-icant performance gains on the CNN/DailyMail corpus compared with the baseline model. Experimental results demonstrate that our method can obtain ROUGE-1 points of 37.48, ROUGE-2 points of 16.40 and ROUGE-L points of 34.36. Our work shows that several features of our proposed model contribute to further improvements in performance.

                  You May Also Like

                  About the Author: nlpvv

                  发表评论

                  玩投彩的女生

                        
                        

                                        
                                        

                                                  电子游艺设施 四川金7乐开奖走势图 ipad百人牛牛 利盈娱乐城真人龙虎斗 体彩20选5计算规则 北京快乐8开奖记录360 ag真人视频怎么玩 法甲射手榜排名 拉趣网提现手续费多少 16122足彩复式推荐 腾讯欢乐升级木蚂蚁下载 北京11选5任五遗漏TOP10 新疆25选7078期开奖 3d村中国福利彩票3 32张扑克牌九技巧