From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

Abstract

Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words. This research topic has started to attract the attention of a large community of researchers, and it is nowadays counted as one of the most promising research areas. In general, text summarization algorithms aim at using a plain text document as input and then output a summary. However, in real-world applications, most of the data is not in a plain text format. Instead, there is much manifold information to be summarized, such as the summary for a web page based on a query in the search engine, extreme long document (e.g., academic paper), dialog history and so on. In this paper, we focus on the survey of these new summarization tasks and approaches in the real-world application.

Accepted by IJCAI 2020 Survey Track

References in corpus (5)

• Sequence to Sequence Learning with Neural Networks
• SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
• SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
• Multi-document abstractive summarization using ILP based multi-sentence compression
• Reader-Aware Multi-Document Summarization via Sparse Coding

Cited by in corpus (2)

• Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization
• The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement