|Title||Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization|
|Affiliation||Peking Univ, Sch EECS, Key Lab Computat Linguist, MOE, Beijing, Peoples R China.|
Peking Univ, Beijing Inst Big Data Res, Deep Learning Lab, Beijing, Peoples R China.
Peking Univ, Sch Foreign Languages, Beijing, Peoples R China.
|Publisher||PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2|
|Citation||PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2. 2018, 725-731.|
|Abstract||Most of the current abstractive text summarization models are based on the sequence-to-sequence model (Seq2Seq). The source content of social media is long and noisy, so it is difficult for Seq2Seq to learn an accurate semantic representation. Compared with the source content, the annotated summary is short and well written. Moreover, it shares the same meaning as the source content. In this work, we supervise the learning of the representation of the source content with that of the summary. In implementation, we regard a summary autoencoder as an assistant supervisor of Seq2Seq. Following previous work, we evaluate our model on a popular Chinese social media dataset. Experimental results show that our model achieves the state-of-the-art performances on the benchmark dataset.(1)|
|Appears in Collections:||信息科学技术学院|