text: The encoder-decoder model is widely used in natural language generation tasks. However, the model sometimes suffers from repeated redundant generation, misses important phrases, and includes irrelevant entities. Toward solving these problems we propose a novel source-side token prediction module. Our method jointly estimates the probability distributions over source and target vocabularies to capture a correspondence between source and target tokens. The experiments show that the proposed model outperforms the current state-of-the-art method in the headline generation task. Additionally, we show that our method has an ability to learn a reasonable token-wise correspondence without knowing any true alignments.
  @cite_7: We describe Abstract Meaning Representation (AMR), a semantic representation language in which we are writing down the meanings of thousands of English sentences. We hope that a sembank of simple, whole-sentence semantic structures will spur new work in statistical natural language understanding and generation, like the Penn Treebank encouraged work on statistical parsing. This paper gives an overview of AMR and tools associated with it.
summary: Apart from the , some studies proposed methods to improve the performance of the headline generation task. incorporated AMR @cite_7 into the encoder to use the syntactic and semantic information of the source. also encoded additional information of the source such as TF-IDF, part-of-speech tags and named entities. modeled the typical structure of a headline, such as Who Action What'' with a variational auto-encoder. These approach improved the performance of the headline generation but it is unclear whether they can reduce the .