The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

Abstract Automatic Text Summarization (ATS) is an important area in Natural Language
Processing (NLP) with the goal of shortening a long text into a more compact version by …

Neural network-based methods for abstractive summarization produce outputs that are more
fluent than other techniques, but which can be poor at content selection. This work proposes …

Neural abstractive summarization models are flexible and can produce coherent summaries,
but they are sometimes unfaithful and can be difficult to control. While previous studies …

In the past few years, neural abstractive text summarization with sequence-to-sequence
(seq2seq) models have gained a lot of popularity. Many interesting techniques have been …

With the evolution of the Internet and multimedia technology, the amount of text data has
increased exponentially. This text volume is a precious source of information and knowledge …

Current summarization systems yield generic summaries that are disconnected from users'
preferences and expectations. To address this limitation, we present CTRLsum, a novel …

In recent years, the volume of textual data has rapidly increased, which has generated a
valuable resource for extracting and analysing information. To retrieve useful knowledge …

We propose Visual News Captioner, an entity-aware model for the task of news image
captioning. We also introduce Visual News, a large-scale benchmark consisting of more …

Natural Language Generation (NLG) has made great progress in recent years due to the
development of deep learning techniques such as pre-trained language models. This …