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. 1 Results show that our learning-based methods have the capability to control length without degrading summary quality in a summarization task.
Length Control neural Encoder-Decoder REF first proposed two learning-based neural encoderdecoder models to control sequence length named LenInit and LenEmb.
Controlling Output Length in Neural Encoder-Decoders
ArXiv
ArXiv
Computer Science