15 December 2015

Automatic Summarization

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
email
web
speech
conversation log
financial data
book
social media
legal

Evaluation:
precision
recall
utility
manual
automatic
pyramid
linguistic quality
accuracy