The dominant graph-to-sequence transduction models employ graph neural networks for
graph representation learning, where the structural information is reflected by the receptive …

Sequence-to-sequence models have shown strong performance across a broad range of
applications. However, their application to parsing and generating text usingAbstract …

We focus on graph-to-sequence learning, which can be framed as transducing graph
structures to sequences for text generation. To capture structural information associated with …

Generating an abstract from a collection of documents is a desirable capability for many real-
world applications. However, abstractive approaches to multi-document summarization have …

Recent studies on AMR-to-text generation often formalize the task as a sequence-to-
sequence (seq2seq) learning problem by converting an Abstract Meaning Representation …

Abstract meaning representation (AMR)-to-text generation is the challenging task of
generating natural language texts from AMR graphs, where nodes represent concepts and …

Recent work on abstractive summarization has made progress with neural encoder-decoder
architectures. However, such models are often challenged due to their lack of explicit …

With an ever increasing size of text present on the Internet, automatic summary generation
remains an important problem for natural language understanding. In this work we explore a …

Recognizing the offensive, abusive, and profanity of multimedia content on the web has
been a challenge to keep the web environment for user's freedom of speech. As profanity …

This paper introduces a simple yet effective transition-based system for Abstract Meaning
Representation (AMR) parsing. We argue that a well-defined search space involved in a …