We present a robust approach for detecting intrinsic sentence importance in
news, by training on two corpora of document-summary pairs. When used for
single-document summarization, our approach, combined with the "beginning of
document" heuristic, outperforms a state-of-the-art summarizer and the
beginning-of-article baseline in both automatic and manual evaluations. These
results represent an important advance because in the absence of cross-document
repetition, single document summarizers for news have not been able to
consistently outperform the strong beginning-of-article baseline.

本研究提出了一种鲁棒的方法，通过训练文档 - 摘要配对的语料库来检测新闻中内在句子的重要性，并结合‘文档开头’启发式方法，用于单篇文档摘要，得到了比现有先进摘要算法和基线方法更好的结果，这是一个重要的进步。

单文档新闻摘要中的（非）重要内容检测

Detecting (Un)Important Content for Single-Document News Summarization

We present a discriminative model for single-document summarization that
integrally combines compression and anaphoricity constraints. Our model selects
textual units to include in the summary based on a rich set of sparse features
whose weights are learned on a large corpus. We allow for the deletion of
content within a sentence when that deletion is licensed by compression rules;
in our framework, these are implemented as dependencies between subsentential
units of text. Anaphoricity constraints then improve cross-sentence coherence
by guaranteeing that, for each pronoun included in the summary, the pronoun's
antecedent is included as well or the pronoun is rewritten as a full mention.
When trained end-to-end, our final system outperforms prior work on both ROUGE
as well as on human judgments of linguistic quality.

本文提出了一种判别模型，它将压缩和先行语限制结合起来，选择文本单元以生成单文档摘要，并在大语料库上学习权重。训练后，该系统在 ROUGE 和人类语言质量判断方面均优于其他已有工作。