Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization
arXiv:1805.04869
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.
arXiv commentaccepted by ACL 2018
References in corpus (9)
- Adam: A Method for Stochastic Optimization
- Neural Machine Translation by Jointly Learning to Align and Translate
- Sequence to Sequence Learning with Neural Networks
- Effective Approaches to Attention-based Neural Machine Translation
- Incorporating Copying Mechanism in Sequence-to-Sequence Learning
- From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews
- Neural Summarization by Extracting Sentences and Words
- Deep Recurrent Generative Decoder for Abstractive Text Summarization
- Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation