Citing opinions is a powerful yet understudied strategy in argumentation. For
example, an environmental activist might say, "Leading scientists agree that
global warming is a serious concern," framing a clause which affirms their own
stance ("that global warming is serious") as an opinion endorsed ("[scientists]
agree") by a reputable source ("leading"). In contrast, a global warming denier
might frame the same clause as the opinion of an untrustworthy source with a
predicate connoting doubt: "Mistaken scientists claim [...]." Our work studies
opinion-framing in the global warming (GW) debate, an increasingly partisan
issue that has received little attention in NLP. We introduce Global Warming
Stance Dataset (GWSD), a dataset of stance-labeled GW sentences, and train a
BERT classifier to study novel aspects of argumentation in how different sides
of a debate represent their own and each other's opinions. From 56K news
articles, we find that similar linguistic devices for self-affirming and
opponent-doubting discourse are used across GW-accepting and skeptic media,
though GW-skeptical media shows more opponent-doubt. We also find that authors
often characterize sources as hypocritical, by ascribing opinions expressing
the author's own view to source entities known to publicly endorse the opposing
view. We release our stance dataset, model, and lexicons of framing devices for
future work on opinion-framing and the automatic detection of GW stance.

本研究介绍了全球变暖立场数据集（GWSD）并训练了 BERT 分类器，研究了不同立场如何代表自己和彼此意见的论证中意见陈述的构建，发现自我肯定和质疑对手的表述在全球变暖接受和怀疑媒体中使用相似的语言方式，但怀疑者更多持反对观点，而作者常常将表达自己观点的意见归于公开支持对立观点的源实体，进而塑造源实体的虚伪形象。

全球变暖媒体立场检测

Detecting Stance in Media on Global Warming

Current research in automatic single document summarization is dominated by
two effective, yet naive approaches: summarization by sentence extraction, and
headline generation via bag-of-words models. While successful in some tasks,
neither of these models is able to adequately capture the large set of
linguistic devices utilized by humans when they produce summaries. One possible
explanation for the widespread use of these models is that good techniques have
been developed to extract appropriate training data for them from existing
document/abstract and document/headline corpora. We believe that future
progress in automatic summarization will be driven both by the development of
more sophisticated, linguistically informed models, as well as a more effective
leveraging of document/abstract corpora. In order to open the doors to
simultaneously achieving both of these goals, we have developed techniques for
automatically producing word-to-word and phrase-to-phrase alignments between
documents and their human-written abstracts. These alignments make explicit the
correspondences that exist in such document/abstract pairs, and create a
potentially rich data source from which complex summarization algorithms may
learn. This paper describes experiments we have carried out to analyze the
ability of humans to perform such alignments, and based on these analyses, we
describe experiments for creating them automatically. Our model for the
alignment task is based on an extension of the standard hidden Markov model,
and learns to create alignments in a completely unsupervised fashion. We
describe our model in detail and present experimental results that show that
our model is able to learn to reliably identify word- and phrase-level
alignments in a corpus of <document,abstract> pairs.

本文主要探讨了目前单文档自动摘要所采用的句子提取和基于词袋模型的标题生成算法的不足，并提出了一种基于词语和短语对齐技术的自动化摘要算法。通过实验结果表明，该模型能够在 <文档，摘要> 数据集中可靠地识别单词和短语的对齐。