Sentences that present a complex syntax act as a major stumbling block for
downstream Natural Language Processing applications whose predictive quality
deteriorates with sentence length and complexity. The task of Text
Simplification (TS) may remedy this situation. It aims to modify sentences in
order to make them easier to process, using a set of rewriting operations, such
as reordering, deletion, or splitting. State-of-the-art syntactic TS approaches
suffer from two major drawbacks: first, they follow a very conservative
approach in that they tend to retain the input rather than transforming it, and
second, they ignore the cohesive nature of texts, where context spread across
clauses or sentences is needed to infer the true meaning of a statement. To
address these problems, we present a discourse-aware TS approach that splits
and rephrases complex English sentences within the semantic context in which
they occur. Based on a linguistically grounded transformation stage that uses
clausal and phrasal disembedding mechanisms, complex sentences are transformed
into shorter utterances with a simple canonical structure that can be easily
analyzed by downstream applications. With sentence splitting, we thus address a
TS task that has hardly been explored so far. Moreover, we introduce the notion
of minimality in this context, as we aim to decompose source sentences into a
set of self-contained minimal semantic units. To avoid breaking down the input
into a disjointed sequence of statements that is difficult to interpret because
important contextual information is missing, we incorporate the semantic
context between the split propositions in the form of hierarchical structures
and semantic relationships. In that way, we generate a semantic hierarchy of
minimal propositions that leads to a novel representation of complex assertions
that puts a semantic layer on top of the simplified sentences.

在语义上意识到文本中的句子结构和上下文联系，通过将复杂句子进行拆分和重新表述，生成有语义层次的简化句子，以提高自然语言处理应用的预测质量。