The goal of multimodal summarization is to extract the most important information from
different modalities to form summaries. Unlike unimodal summarization, the multimodal …

Neural extractive summarization models usually employ a hierarchical encoder for
document encoding and they are trained using sentence-level labels, which are created …

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 …

Despite the success achieved in neural abstractive summarization based on pre-trained
language models, one unresolved issue is that the generated summaries are not always …

A popular multimedia news format nowadays is providing users with a lively video and a
corresponding news article, which is employed by influential news media including CNN …

Creating presentation materials requires complex multimodal reasoning skills to summarize
key concepts and arrange them in a logical and visually pleasing manner. Can machines …

Given a set of related publications, related work section generation aims to provide
researchers with an overview of the specific research area by summarizing these works and …

Dialogue, the most fundamental and specially privileged arena of language, gains
increasing ubiquity across the Web in recent years. Quickly going through the long dialogue …

The related work section is an important component of a scientific paper, which highlights
the contribution of the target paper in the context of the reference papers. Authors can save …

In neural abstractive summarization field, conventional sequence-to-sequence based
models often suffer from summarizing the wrong aspect of the document with respect to the …