Neural Headline Generation on Abstract Meaning Representation
Sho Takase, Jun Suzuki, Naoaki Okazaki, Tsutomu Hirao, Masaaki Nagata · 2016
Neural network-based encoder-decoder models are among recent attractive methodologies for tackling natural language generation tasks.This paper investigates the usefulness of structural syntactic and semantic information additionally incorporated in a baseline neural attention-based model.We encode results obtained from an abstract meaning representation (AMR) parser using a modified version of Tree-LSTM.Our proposed attention-based AMR encoder-decoder model improves headline generation benchmarks compared with the baseline neural attention-based model.