We present a new neural model for text summarization that first extracts sentences from a
document and then compresses them. The proposed model offers a balance that sidesteps …

Currently, no large-scale training data is available for the task of scientific paper
summarization. In this paper, we propose a novel method that automatically generates …

Graphs that capture relations between textual units have great benefits for detecting salient
information from multiple documents and generating overall coherent summaries. In this …

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

We present a method to produce abstractive summaries of long documents that exceed
several thousand words via neural abstractive summarization. We perform a simple …

Background and Objectives: Text summarization plays an essential role in reducing time
and cost in many domains such as medicine, engineering, etc. On the other hand, manual …

Extractive summarization models require sentence-level labels, which are usually created
heuristically (eg, with rule-based methods) given that most summarization datasets only …

Current summarization systems only produce plain, factual headlines, but do not meet the
practical needs of creating memorable titles to increase exposure. We propose a new task …

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

Progress in summarizing long texts is inhibited by the lack of appropriate evaluation
frameworks. When a long summary must be produced to appropriately cover the facets of …