Automated Text-Item(s) Summarization Task
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An Automated Text-Item(s) Summarization Task is a text summarization task that is an automated NLG task (to create a text summary).
- Context:
- It can (typically) be solved by a Text Summarization System (that implements a text summarization algorithm).
- It can range from being a Zero-Shot Text Summarization Task to being a Few-Shot Text Summarization Task to being a Supervised Text Summarization Task.
- It can range from being an Open-Topic Automated Text-Item(s) Summarization Task to being a Topic-Focused Automated Text-Item(s) Summarization Task.
- It can range from being a Automated Short-Text Summarization Task to being a Automated Document Summarization Task.
- It can range from being a Automated Single-Text Item Summarization Task to being a Automated Multi-Text Item Summarization Task.
- …
- Example(s):
- Counter-Example(s):
- See: Automated Summarization, Information Overload, Data-Driven Text Summarization.
References
2023
- (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Automatic_summarization Retrieved:2023-9-16.
- Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data.
Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document.[1] On the other hand, visual content can be summarized using computer vision algorithms. Image summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection. Video summarization algorithms identify and extract from the original video content the most important frames (key-frames), and/or the most important video segments (key-shots), normally in a temporally ordered fashion.[2] [3] [4] [5] Video summaries simply retain a carefully selected subset of the original video frames and, therefore, are not identical to the output of video synopsis algorithms, where new video frames are being synthesized based on the original video content.
- Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different types of data.
- ↑ Torres-Moreno, Juan-Manuel (1 October 2014). Automatic Text Summarization. Wiley. pp. 320–. ISBN 978-1-848-21668-6.
- ↑ Sankar K. Pal; Alfredo Petrosino; Lucia Maddalena (25 January 2012). Handbook on Soft Computing for Video Surveillance. CRC Press. pp. 81–. ISBN 978-1-4398-5685-7.
- ↑ Elhamifar, Ehsan; Sapiro, Guillermo; Vidal, Rene (2012). “See all by looking at a few: Sparse modeling for finding representative objects". 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1600–1607. doi:10.1109/CVPR.2012.6247852. ISBN 978-1-4673-1228-8. S2CID 5909301. Retrieved 4 December 2022.
- ↑ Mademlis, Ioannis; Tefas, Anastasios; Nikolaidis, Nikos; Pitas, Ioannis (2016). "Multimodal stereoscopic movie summarization conforming to narrative characteristics". IEEE Transactions on Image Processing. IEEE. 25 (12): 5828–5840. Bibcode:2016ITIP...25.5828M. doi:10.1109/TIP.2016.2615289. hdl:1983/2bcdd7a5-825f-4ac9-90ec-f2f538bfcb72. PMID 28113502. S2CID 18566122. Retrieved 4 December 2022.
- ↑ Mademlis, Ioannis; Tefas, Anastasios; Pitas, Ioannis (2018). "A salient dictionary learning framework for activity video summarization via key-frame extraction". Information Sciences. Elsevier. 432: 319–331. doi:10.1016/j.ins.2017.12.020. Retrieved 4 December 2022.
2011
- (Wikipedia, 2011) ⇒ “http://en.wikipedia.org/wiki/Automatic_summarization#Document_Summarization Document Summarization.” Wikipedia - The Free Encyclopedia (accessed 2011-Jun-13).
- … Like keyphrase extraction, document summarization hopes to identify the essence of a text. The only real difference is that now we’re dealing with larger text units — whole sentences instead of words and phrases. While some work has been done in abstractive summarization (creating an abstract synopsis like that of a human), the majority of summarization systems are extractive (selecting a subset of sentences to place in a summary).
2004
- (Hu & Liu, 2004) ⇒ Minqing Hu, Bing Liu. (2004). “Mining and Summarizing Customer Reviews.” In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004) doi:10.1145/1014052.1014073
2002
- (Radev et al., 2002) ⇒ Dragomir Radev, Eduard Hovy, and Kathleen R. McKeown. (2002). “Introduction to the Special Issue on Summarization.” In: Computational Linguistics, 28(4). doi:10.1162/089120102762671927
2001
- (Mani, 2001) ⇒ Inderjeet Mani. (2001). “Automatic Summarization.” John Benjamins Publishing Company. ISBN:9027249865
- QUOTE: … Informally, the goal of text summarization is to take a textual document, extract content from it and present the most important content to the user in a condensed form and in a manner sensitive to the user's or application's needs [13].
2000 =
- (Hahn & Mani, 2000) ⇒ Udo Hahn, and Inderjeet Mani. (2000). “The Challenges of Automatic Summarization.” In: Computer Journal, 33(11). doi:10.1109/2.881692
- QUOTE: Summarization -- the art of abstracting key content from one or more information sources -- has become an integral part of everyday life. People keep abreast of world affairs by listening to news bites.
1999
- (Mani & Maybury, 1999) ⇒ Inderjeet Mani (editor), Mark T. Maybury (editor). (1999). “Advances in Automatic Text Summarization.” In: MIT Press. ISBN:0262133598
- QUOTE: The book is organized into six sections: Classical Approaches, Corpus-based Approaches, Exploiting Discourse Structure, Knowledge-Rich Approaches, Evaluation Methods, and New Summarization Problem Areas.
1982
- (DeJong, 1982) ⇒ G. F. DeJong. (1982). “An overview of the FRUMP system.” In: Strategies for Natural Language Processing, W.G.Lehnert & M.H.Ringle (Eds).
- Domain specific
- Skimmed and summarised news articles.
- Template instantiation system
- Identified which articles belonged to a particular domain.