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

arXiv: https://arxiv.org/abs/2005.04684 · HTML: https://mlsanity.com/paper/2005.04684/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2020.

## Built on by (most cited first, 16 of 16)

- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs](https://mlsanity.com/paper/2104.08400/) (2021)
- [EmailSum: Abstractive Email Thread Summarization](https://mlsanity.com/paper/2107.14691/) (2021)
- [Target-aware Abstractive Related Work Generation with Contrastive Learning](https://mlsanity.com/paper/2205.13339/) (2022)
- [Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition](https://mlsanity.com/paper/2108.00194/) (2021)
- [A Topic-aware Summarization Framework with Different Modal Side Information](https://mlsanity.com/paper/2305.11503/) (2023)
- [Scientific Paper Extractive Summarization Enhanced by Citation Graphs](https://mlsanity.com/paper/2212.04214/) (2022)
- [HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling](https://mlsanity.com/paper/2112.13597/) (2021)
- [Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition](https://mlsanity.com/paper/2303.04896/) (2023)
- [Improving Factual Consistency of Abstractive Summarization on Customer Feedback](https://mlsanity.com/paper/2106.16188/) (2021)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [UMSE: Unified Multi-scenario Summarization Evaluation](https://mlsanity.com/paper/2305.16895/) (2023)
- [Dual Embodied-Symbolic Concept Representations for Deep Learning](https://mlsanity.com/paper/2203.00600/) (2022)
- [Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures](https://mlsanity.com/paper/2204.11190/) (2022)
- [Affective Feedback Synthesis Towards Multimodal Text and Image Data](https://mlsanity.com/paper/2203.12692/) (2022)
- [Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data](https://mlsanity.com/paper/2402.07640/) (2024)

## Abstract (arXiv)

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.