21 citations · 57 across the 16 of their papers we have counts for
5 papers · 2 filters
Source-side Prediction for Neural Headline Generation
Shun Kiyono, Sho Takase, Jun Suzuki +3
The encoder-decoder model is widely used in natural language generation tasks. However, the model sometimes suffers from repeated redundant generation, misses important phrases, an…
Input-to-Output Gate to Improve RNN Language Models
Sho Takase, Jun Suzuki, Masaaki Nagata
This paper proposes a reinforcing method that refines the output layers of existing Recurrent Neural Network (RNN) language models. We refer to our proposed method as Input-to-Outp…
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Jun Suzuki, Masaaki Nagata
This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound…
Reading Comprehension using Entity-based Memory Network
Xun Wang, Katsuhito Sudoh, Masaaki Nagata +3
This paper introduces a novel neural network model for question answering, the \emph{entity-based memory network}. It enhances neural networks' ability of representing and calculat…
Enumeration of Extractive Oracle Summaries
Tsutomu Hirao, Masaaki Nishino, Jun Suzuki +1
To analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extract…