Reading levels are highly individual and can depend on a text's language, a
person's cognitive abilities, or knowledge on a topic. Text simplification is
the task of rephrasing a text to better cater to the abilities of a specific
target reader group. Simplification of scientific abstracts helps non-experts
to access the core information by bypassing formulations that require domain or
expert knowledge. This is especially relevant for, e.g., cancer patients
reading about novel treatment options. The SimpleText lab hosts the
simplification of scientific abstracts for non-experts (Task 3) to advance this
field. We contribute three runs employing out-of-the-box summarization models
(two based on T5, one based on PEGASUS) and one run using ChatGPT with complex
phrase identification.

科学摘要简化是为非专家出版物提供核心信息的重要任务，包括使用简化模型和聊天 GPT 进行摘要。

科学文本简化用于非专业读者

Text Simplification of Scientific Texts for Non-Expert Readers

Text simplification research has mostly focused on sentence-level
simplification, even though many desirable edits - such as adding relevant
background information or reordering content - may require document-level
context. Prior work has also predominantly framed simplification as a
single-step, input-to-output task, only implicitly modeling the fine-grained,
span-level edits that elucidate the simplification process. To address both
gaps, we introduce the SWiPE dataset, which reconstructs the document-level
editing process from English Wikipedia (EW) articles to paired Simple Wikipedia
(SEW) articles. In contrast to prior work, SWiPE leverages the entire revision
history when pairing pages in order to better identify simplification edits. We
work with Wikipedia editors to annotate 5,000 EW-SEW document pairs, labeling
more than 40,000 edits with proposed 19 categories. To scale our efforts, we
propose several models to automatically label edits, achieving an F-1 score of
up to 70.6, indicating that this is a tractable but challenging NLU task.
Finally, we categorize the edits produced by several simplification models and
find that SWiPE-trained models generate more complex edits while reducing
unwanted edits.

引入 SWiPE 数据集，通过整个修订历史，重构从英语维基百科文章到 Simple 维基百科文章的文档级简化过程，并分类模型产生的编辑，发现 SWiPE 训练的模型生成更复杂的编辑，同时减少不需要的编辑。