The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting
This addresses a fundamental assumption in text simplification for improving readability, but the findings are incremental as they refine existing methods rather than introducing new paradigms.
The paper tackled the problem of whether splitting sentences improves readability, finding that bisecting sentences enhances readability more than trisecting them, with Bayesian modeling providing clear evidence.
In this work, we focus on sentence splitting, a subfield of text simplification, motivated largely by an unproven idea that if you divide a sentence in pieces, it should become easier to understand. Our primary goal in this paper is to find out whether this is true. In particular, we ask, does it matter whether we break a sentence into two or three? We report on our findings based on Amazon Mechanical Turk. More specifically, we introduce a Bayesian modeling framework to further investigate to what degree a particular way of splitting the complex sentence affects readability, along with a number of other parameters adopted from diverse perspectives, including clinical linguistics, and cognitive linguistics. The Bayesian modeling experiment provides clear evidence that bisecting the sentence leads to enhanced readability to a degree greater than what we create by trisection.