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 …

With the rapid development of the Internet, the massive amount of web textual data has
grown exponentially, which has brought considerable challenges to downstream tasks, such …

Text style transfer is an important task in natural language generation, which aims to control
certain attributes in the generated text, such as politeness, emotion, humor, and many …

It is well-known that abstractive summaries are subject to hallucination---including material
that is not supported by the original text. While summaries can be made hallucination-free by …

In neural abstractive summarization, the generated summaries often face semantic
irrelevance and content deviation from the input sentences. In this work, we incorporate …

An abstract must not change the meaning of the original text. A single most effective way to
achieve that is to increase the amount of copying while still allowing for text abstraction …

In e-commerce portals, generating answers for product-related questions has become a
crucial task. In this article, we focus on the task of product-aware answer generation, which …

Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and
learn a model to edit it to produce the final output. We propose to extend this framework with …

Most studies on abstractive summarization report ROUGE scores between system and
reference summaries. However, we have a concern about the truthfulness of generated …

Developers insert logging statements in the source code to collect important runtime
information about software systems. The textual descriptions in logging statements (ie …