Graph-based text representation is one of the important preprocessing steps in data and text
mining, Natural Language Processing (NLP), and information retrieval approaches. The …

In this paper, we focus on learning structure-aware document representations from data
without recourse to a discourse parser or additional annotations. Drawing inspiration from …

We propose a local coherence model that captures the flow of what semantically connects
adjacent sentences in a text. We represent the semantics of a sentence by a vector and …

Automatic Readability Assessment (ARA), the task of assigning a reading level to a text, is
traditionally treated as a classification problem in NLP research. In this paper, we propose …

We present a multiattentive recurrent neural network architecture for automatic multilingual
readability assessment. This architecture considers raw words as its main input, but …

To date there has been very little work on assessing discourse coherence methods on real-
world data. To address this, we present a new corpus of real-world texts (GCDC) as well as …

Previous work on automatic summarization does not thoroughly consider coherence while
generating the summary. We introduce a graph-based approach to summarize scientific …

Coherence is an important aspect of text quality, and various approaches have been applied
to coherence modeling. However, existing methods solely focus on a single document's …

Coherence is an important aspect of text quality and is crucial for ensuring its readability. It is
essential desirable for outputs from text generation systems like summarization, question …

Coherence is established by semantic connections between sentences of a text which can
be modeled by lexical relations. In this paper, we introduce the lexical coherence graph …