Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

In this paper we conceptualize singledocument extractive summarization as a tree induction
problem. In contrast to previous approaches (Marcu, 1999; Yoshida et al., 2014) which have …

Neural network based deep learning methods aim to learn representations of data and have
produced state-of-the-art results in many natural language processing (NLP) tasks …

Sentence extractive summarization shortens a document by selecting sentences for a
summary while preserving its important contents. However, constructing a coherent and …

We introduce a generic seq2seq parsing framework that casts constituency parsing
problems (syntactic and discourse parsing) into a series of conditional splitting decisions …

Hierarchical discourse structures benefit Natural Language Understanding tasks, such as
text summarization and sentiment analysis. Rhetorical Structure Theory (RST) is particularly …

Recently, span-based constituency parsing has achieved competitive accuracies with
extremely simple models by using bidirectional RNNs to model “spans”. However, the …

In this Chapter we learn how to manage a dialogue relying on discourse of its utterances.
We first explain how to build an invariant discourse tree for a corpus of texts to arrange a …

A growing trend of extractive summarization research is to take the document structure into
account which has been shown to correlate with the important contents in a text. However …

Code-mixing or language-mixing is a linguistic phenomenon where multiple language mix
together during conversation. Standard natural language processing (NLP) tools such as …