Segmenting text into fine-grained units of meaning is important to a wide
range of NLP applications. The default approach of segmenting text into
sentences is often insufficient, especially since sentences are usually complex
enough to include multiple units of meaning that merit separate treatment in
the downstream task. We focus on the task of abstractive proposition
segmentation: transforming text into simple, self-contained, well-formed
sentences. Several recent works have demonstrated the utility of proposition
segmentation with few-shot prompted LLMs for downstream tasks such as
retrieval-augmented grounding and fact verification. However, this approach
does not scale to large amounts of text and may not always extract all the
facts from the input text. In this paper, we first introduce evaluation metrics
for the task to measure several dimensions of quality. We then propose a
scalable, yet accurate, proposition segmentation model. We model proposition
segmentation as a supervised task by training LLMs on existing annotated
datasets and show that training yields significantly improved results. We
further show that by using the fine-tuned LLMs as teachers for annotating large
amounts of multi-domain synthetic distillation data, we can train smaller
student models with results similar to the teacher LLMs. We then demonstrate
that our technique leads to effective domain generalization, by annotating data
in two domains outside the original training data and evaluating on them.
Finally, as a key contribution of the paper, we share an easy-to-use API for
NLP practitioners to use.

将文本分成细粒度的含义单元对于广泛的自然语言处理应用非常重要，本文重点讨论了抽象命题分割的任务，并提出了评估指标和可扩展准确的命题分割模型，同时介绍了领域泛化方法，并分享了一个简单易用的 API。