Automatic dialogue summarization is a well-established task that aims to
identify the most important content from human conversations to create a short
textual summary. Despite recent progress in the field, we show that most of the
research has focused on summarizing the factual information, leaving aside the
affective content, which can yet convey useful information to analyse, monitor,
or support human interactions. In this paper, we propose and evaluate a set of
measures $PEmo$, to quantify how much emotion is preserved in dialog summaries.
Results show that, summarization models of the state-of-the-art do not preserve
well the emotional content in the summaries. We also show that by reducing the
training set to only emotional dialogues, the emotional content is better
preserved in the generated summaries, while conserving the most salient factual
information.

自动对话摘要是一个已经建立良好的任务，旨在从人类对话中识别出最重要的内容，创建一个简短的文本摘要。尽管该领域最近取得了进展，但我们发现大部分研究都集中在总结事实信息上，忽略了能够传达有用信息以分析、监控或支持人类互动的情感内容。本文提出并评估了一组度量标准 $PEmo$，以量化对话摘要中保留的情感程度。结果表明，现有的摘要模型在摘要中并不能很好地保留情感内容。我们还展示了通过将训练集仅限于情感对话，生成的摘要可以更好地保留情感内容，同时保持最突出的事实信息。

对话摘要中的情感细微差别评估

Evaluating Emotional Nuances in Dialogue Summarization

When humans judge the affective content of texts, they also implicitly assess
the correctness of such judgment, that is, their confidence. We hypothesize
that people's (in)confidence that they performed well in an annotation task
leads to (dis)agreements among each other. If this is true, confidence may
serve as a diagnostic tool for systematic differences in annotations. To probe
our assumption, we conduct a study on a subset of the Corpus of Contemporary
American English, in which we ask raters to distinguish neutral sentences from
emotion-bearing ones, while scoring the confidence of their answers. Confidence
turns out to approximate inter-annotator disagreements. Further, we find that
confidence is correlated to emotion intensity: perceiving stronger affect in
text prompts annotators to more certain classification performances. This
insight is relevant for modelling studies of intensity, as it opens the
question wether automatic regressors or classifiers actually predict intensity,
or rather human's self-perceived confidence.

人类判断文本情感内容时，情感强度影响了他们的自信程度，影响了标注结果的一致性，而这一现象对情感强度的建模有重要影响。

情感评分：强度、注释置信度和一致性的纠缠

Emotion Ratings: How Intensity, Annotation Confidence and Agreements are  Entangled

The majority of current systems for end-to-end dialog generation focus on
response quality without an explicit control over the affective content of the
responses. In this paper, we present an affect-driven dialog system, which
generates emotional responses in a controlled manner using a continuous
representation of emotions. The system achieves this by modeling emotions at a
word and sequence level using: (1) a vector representation of the desired
emotion, (2) an affect regularizer, which penalizes neutral words, and (3) an
affect sampling method, which forces the neural network to generate diverse
words that are emotionally relevant. During inference, we use a reranking
procedure that aims to extract the most emotionally relevant responses using a
human-in-the-loop optimization process. We study the performance of our system
in terms of both quantitative (BLEU score and response diversity), and
qualitative (emotional appropriateness) measures.

本文介绍了一种能够控制情感内容的情感驱动对话系统的方法，通过使用连续的情感表示来模拟词和句子级别的情感，并在推理过程中使用重新排名程序来获取最具情感相关性的响应。

情感驱动的对话生成

Affect-Driven Dialog Generation

Existing neural conversational models process natural language primarily on a
lexico-syntactic level, thereby ignoring one of the most crucial components of
human-to-human dialogue: its affective content. We take a step in this
direction by proposing three novel ways to incorporate affective/emotional
aspects into long short term memory (LSTM) encoder-decoder neural conversation
models: (1) affective word embeddings, which are cognitively engineered, (2)
affect-based objective functions that augment the standard cross-entropy loss,
and (3) affectively diverse beam search for decoding. Experiments show that
these techniques improve the open-domain conversational prowess of
encoder-decoder networks by enabling them to produce emotionally rich responses
that are more interesting and natural.

通过引入情感方面的三种新方法，可以改善神经会话模型的自然语言处理能力，实现更加丰富、有趣和自然的情感化响应。