Repetition in natural language generation reduces the informativeness of text and makes it less appealing. Various techniques have been proposed to alleviate it. In this work, we explore and propose techniques to reduce repetition in abstractive summ
arization. First, we explore the application of unlikelihood training and embedding matrix regularizers from previous work on language modeling to abstractive summarization. Next, we extend the coverage and temporal attention mechanisms to the token level to reduce repetition. In our experiments on the CNN/Daily Mail dataset, we observe that these techniques reduce the amount of repetition and increase the informativeness of the summaries, which we confirm via human evaluation.
