Text simplification rewrites text to be more readable for a specific
audience, while preserving its meaning. However, determining what makes a text
easy to read depends on who are the intended readers. Recent work has
introduced a wealth of techniques to control output simplicity, ranging from
specifying the desired reading grade level to providing control tokens that
directly encode low-level simplification edit operations. However, it remains
unclear how to set the input parameters that control simplification in
practice. Existing approaches set them at the corpus level, disregarding the
complexity of individual source text, and do not directly evaluate them at the
instance level. In this work, we conduct an empirical study to understand how
different control mechanisms impact the adequacy and simplicity of model
outputs. Based on these insights, we introduce a simple method for predicting
control tokens at the sentence level to enhance the quality of the simplified
text. Predicting control token values using features extracted from the
original complex text and a user-specified degree of complexity improves the
quality of the simplified outputs over corpus-level search-based heuristics.

本研究通过实证研究不同的控制机制对模型简化输出的适当性和简单性影响的方式，提出了一种简单的方法来预测控制令牌，以提高简化文本的质量。

如何控制文本简化？ 对含义保持控制性简化的控制标记的实证研究

How To Control Text Simplification? An Empirical Study of Control Tokens  for Meaning Preserving Controlled Simplification

The finetuning of pretrained transformer-based language generation models are
typically conducted in an end-to-end manner, where the model learns to attend
to relevant parts of the input by itself. However, there does not exist a
mechanism to directly control the model's focus. This work aims to develop a
control mechanism by which a user can select spans of context as "highlights"
for the model to focus on, and generate relevant output. To achieve this goal,
we augment a pretrained model with trainable "focus vectors" that are directly
applied to the model's embeddings, while the model itself is kept fixed. These
vectors, trained on automatic annotations derived from attribution methods, act
as indicators for context importance. We test our approach on two core
generation tasks: dialogue response generation and abstractive summarization.
We also collect evaluation data where the highlight-generation pairs are
annotated by humans. Our experiments show that the trained focus vectors are
effective in steering the model to generate outputs that are relevant to
user-selected highlights.

该研究旨在开发一种控制机制，使用户可以选择上下文的一部分作为 “亮点”，以便生成相关的输出。研究使用可训练的 “焦点向量” 来指示上下文的重要性，测试其在对话响应生成和提取式摘要生成任务中的有效性。