Automatic Summarization is a valuable aspect of Information Extraction in Natural Language Processing. It is applied within Information Retrieval, news summaries, building research abstracts, and within various knowledge exploration contexts. Automatic Summarization can be applied either over single or multiple documents. There is even aspect of building extractions over simple verses rich textual documents. The following extrapolate the various aspects concerning Automatic Summarization processes that are under active research and utilized for development within the various textual domain contexts.
Summarization Types:
| extractive |
| abstractive |
| single document |
| multi-document |
| indicative |
| informative |
| keyword |
| headline |
| generic |
| query-focused |
| update |
| main point |
| key point |
| outline |
| descriptive |
Summary Sentence Approaches:
| revision |
| ordering |
| fusion |
| compression |
| sentence selection vs summary selection |
Unsupervised Methods:
| word frequency |
| word probability |
| tf*idf weighting |
| log-likelihood ratio for topic signatures |
| sentence clustering |
| graph based methods for sentence ranks |
Semantics and Discourse:
| lexical chaining |
| latent semantic analysis |
| coreference |
| rhetorical structure |
| discourse-driven graph representation |
Summary Generation Methods:
| compression |
| rule-based compression |
| statistical compression |
| headline |
| fusion |
| context dependent revision |
| ordering of information |
Various Genre and Domains:
| medical |
| journal article |
| news |
| web |
| speech |
| conversation log |
| financial data |
| book |
| social media |
| legal |
Evaluation:
| precision |
| recall |
| utility |
| manual |
| automatic |
| pyramid |
| linguistic quality |
| accuracy |
