We propose a new length-controllable abstractive summarization model. Recent
state-of-the-art abstractive summarization models based on encoder-decoder
models generate only one summary per source text. However, controllable
summarization, especially of the length, is an important aspect for practical
applications. Previous studies on length-controllable abstractive summarization
incorporate length embeddings in the decoder module for controlling the summary
length. Although the length embeddings can control where to stop decoding, they
do not decide which information should be included in the summary within the
length constraint. Unlike the previous models, our length-controllable
abstractive summarization model incorporates a word-level extractive module in
the encoder-decoder model instead of length embeddings. Our model generates a
summary in two steps. First, our word-level extractor extracts a sequence of
important words (we call it the "prototype text") from the source text
according to the word-level importance scores and the length constraint.
Second, the prototype text is used as additional input to the encoder-decoder
model, which generates a summary by jointly encoding and copying words from
both the prototype text and source text. Since the prototype text is a guide to
both the content and length of the summary, our model can generate an
informative and length-controlled summary. Experiments with the CNN/Daily Mail
dataset and the NEWSROOM dataset show that our model outperformed previous
models in length-controlled settings.

本文提出了一种新的长度可控的抽象汇总模型，与以往仅根据编码器 - 解码器模型生成一篇总结不同，我们的模型在编码器 - 解码器模型中结合了单词级别的抽取模块，通过提取重要的词来控制长度，生成了一个同时具有信息和长度控制的汇总。