论文标题
无监督的句子摘要的离散优化，用单词级提取
Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction
论文作者
论文摘要
自动句子摘要产生句子的较短版本，同时保留其最重要的信息。一个好的摘要的特点是语言流利性和高信息与源句子重叠。我们在无监督的目标函数中对这两个方面进行建模，包括语言建模和语义相似性指标。我们通过离散优化搜索一个高分摘要。根据Rouge分数，我们提出的方法实现了无监督的句子摘要的新最新技术。此外，我们证明了通常报道的Rouge F1度量对摘要长度敏感。由于在最近的工作中不愿利用这一点，因此我们强调，未来的评估应通过输出长度括号明确地分组汇总系统。
Automatic sentence summarization produces a shorter version of a sentence, while preserving its most important information. A good summary is characterized by language fluency and high information overlap with the source sentence. We model these two aspects in an unsupervised objective function, consisting of language modeling and semantic similarity metrics. We search for a high-scoring summary by discrete optimization. Our proposed method achieves a new state-of-the art for unsupervised sentence summarization according to ROUGE scores. Additionally, we demonstrate that the commonly reported ROUGE F1 metric is sensitive to summary length. Since this is unwillingly exploited in recent work, we emphasize that future evaluation should explicitly group summarization systems by output length brackets.
