Controlling Output Length in Neural Encoder Decoders
Neural encoder decoder models have shown great success in many sequence generation tasks. However, previous work has not investigated situations in which we would like to control the length of encoder decoder outputs. This capability is crucial for applications such as text summarization, in which we have to generate concise summaries with a desired length. In this paper, we propose methods for controlling the output sequence length for neural encoder decoder models: two decoding based methods and two learning based methods. Results show that our learning based methods have the capability to control length without degrading summary quality in a summarization task.
EMNLP