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 …

Selecting the “right” amount of information to include in a summary is a difficult task. A good
summary should be detailed and entity-centric without being overly dense and hard to …

Extractive summarization is a crucial task in natural language processing that aims to
condense long documents into shorter versions by directly extracting sentences. The recent …

Multi-task learning (MTL) has become increasingly popular in natural language processing
(NLP) because it improves the performance of related tasks by exploiting their …

Copy module has been widely equipped in the recent abstractive summarization models,
which facilitates the decoder to extract words from the source into the summary. Generally …

The impression section of a radiology report summarizes the most prominent observation
from the findings section and is the most important section for radiologists to communicate to …

Extractive summarization aims to form a summary by directly extracting sentences from the
source document. Existing works mostly formulate it as a sequence labeling problem by …

Extractive summarization for long documents is challenging due to the extended structured
input context. The long-distance sentence dependency hinders cross-sentence relations …

Abstractive summarization aims to generate a concise summary covering salient content
from single or multiple text documents. Many recent abstractive summarization methods are …