Text Summarization Algorithm

A Text Summarization Algorithm is an NLG algorithm that can be implemented by a text summarization system (to solve an automated text summarization task).

• Context:
  • 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.
  • ...
• Example(s):
  • MEAD Algorithm.
  • SQuASH Algorithm.
  • Surveyor Algorithm.
  • a Neural Summarization Algorithm, such as: an LLM-based Text Summarization Algorithm.
  • …
• Counter-Example(s):
  • a Question-Answering Algorithm.
  • an Information Extraction Algorithm.
  • a Document Clustering Algorithm.
  • a Keyphrase Extraction Task.
• See: Corpus-based Text Summarization Algorithm, Discourse Structure-based Text Summarization Algorithm, Knowledge-Rich Text Summarization Algorithm, Text Summarization Algorithm Performance Metric.

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References

2014

• (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.

2007

• (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 Literature Survey for the Language and Statistics II course at CMU, November, 2007 (unpublished).

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

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.

1997

• (Marcu, 1997) ⇒ Daniel Marcu. (1997). “The Rhetorical Parsing, Summarization, and Generation of Natural Language Texts." PhD Thesis. University of Toronto.

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.

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