Text Summarization Algorithm
- It can range from being an Extractive Text Summarization Algorithm to being an Abstractive Text Summarization Algorithm.
- It can range from being a Single-Document Summarization Algorithm to being a Multi-Document Summarization Algorithm.
- It can range from being a Heuristic Text Summarization Algorithm to being a Data-Driven Text Summarization Algorithm.
- It can range from being a Knowledge-Poor Text Summarization Algorithm to being a Knowledge-Rich Text Summarization Algorithm.
- See: Corpus-based Text Summarization Algorithm, Discourse Structure-based Text Summarization Algorithm, Knowledge-Rich Text Summarization Algorithm, Text Summarization Algorithm Performance Metric.
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Automatic_summarization Retrieved:2014-9-10.
- … Generally, there are two approaches to automatic summarization: extraction and abstraction. Extractive methods work by selecting a subset of existing words, phrases, or sentences in the original text to form the summary. In contrast, abstractive methods build an internal semantic representation and then use natural language generation techniques to create a summary that is closer to what a human might generate. Such a summary might contain words not explicitly present in the original. Research into abstractive methods is an increasingly important and active research area, however due to complexity constraints, research to date has focused primarily on extractive methods.
- (Das & Martins, 2007) ⇒ Dipanjan Das, and André F. T. Martins. (2007). “A Survey on Automatic Text Summarization." Literature Survey for the Language and Statistics II course at CMU, November, 2007 (unpublished).
- (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
- (Mani, 2001) ⇒ Inderjeet Mani. (2001). “Automatic Summarization." John Benjamins Publishing Company. ISBN:9027249865
- (Mani & Maybury, 1999) ⇒ Inderjeet Mani (editor), Mark T. Maybury (editor). (1999). “Advances in Automatic Text Summarization.” In: MIT Press. ISBN:0262133598
- (Marcu, 1997) ⇒ Daniel Marcu. (1997). “The Rhetorical Parsing, Summarization, and Generation of Natural Language Texts." PhD Thesis. University of Toronto.
- (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.