Neural Headline Generation on Abstract Meaning Representation
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.
EMNLP