Naive Algorithms for Key-phrase Extraction and Text Summarization from a Single Document inspired by the Protein Biosynthesis Process

1
Naive Algorithms for Key-phrase Extraction and
Text Summarization from a Single Document
inspired by the Protein Biosynthesis Process

• Daniel Gayo Avello
• (University of Oviedo)

2
Whats the problem?
?

• Document reading is a time consuming task
• Many common documents (e.g., e-mail, newsgroup
  posts, web pages) lack of abstract or keywords
• But, they are electronic so we can work on
  them in some way

?
?
8
3
Whats the problem? (cont.)

• Many techniques to perform several Natural
  Language Processing (NLP) useful tasks
• Language identification.
• Document categorization and clustering.
• Keyword extraction.
• Text summarization.
• Quite different
• With/Without human supervision.
• With/Without training.
• With/Without complex linguistic data.
• With/Without document corpora.

?
?
17
4
Any suggestion?

• It would be great to use only one technique to
  carry out several of those tasks.
• Desirable goals
• Simple (only free text, not linguistic data)
• Fully automatic (neither supervision nor ad hoc
  heuristics)
• Scalable (from one web page to several web sites)
• Could it be a bio-inspired solution?

25
5
Our (bio-inspired) hypothesis

• Living beings are defined by their genome.
• Document from a corpus Individual from a
  population
• So?
• Lets imagine a document genome
• Similar documents (similar language/topic) ?
  Similar genomes.
• More interesting, translation from document
  genome to significance proteins (i.e.,
  keyphrases and summaries).

33
6
Our biological inspiration

• The protein biosynthesis process

Termination
Elongation
Initiation
Could we mimic this to distill from a single
document keyphrases and summaries!?
Polypeptide chain
Transcription
42
7
The ingredients
Biological element Computational counterpart
tRNA Spliced document genome
mRNA Documents plain text
Ribosome Algorithm
Polypeptide chain Document chunks with significance weights
Protein Keyphrases
50
8
A DNA for Natural Language?

• n-grams (slices of adjoining n characters)
• Frequency not the most relevant weight for each
  n-gram.
• There exist different measures to show relation
  between both elements in a bigram
• Mutual information.
• Dice coefficient.
• Loglike.
• Cannot be applied straightforward to n-grams ?
• But, they can be generalized (Ferreira and
  Pereira, 1999) ?

58
9
A DNA for Natural Language? (cont.)
67
10
Document genome translation

• So
• Document genome spliced into pseudo-tRNA.
• Document used as pseudo-mRNA.
• We attach to the document pseudo-tRNA
  molecules (with max. weight) while average
  significance per character continues growing.
• Result Document spliced into chunks with
  maximum average significance.
• The
• rain
• in
• Spain
• stays mainly in
• the plain

20 The
49 The r
73 The ra
pseudo-mRNA
The rain in Spain stays mainly in the plain.
etc.
75
11
Folding the protein / summarization

• To obtain keyphrases the protein (text chunks)
  must be folded
• At this moment we are studying different
  alternatives
• Mutual reinforcement?
• Chunks Documents ? Apply classical IR
  techniques?
• Others?
• Automatic text summarization
• Simple but useful approach.
• Use the shortest paragraphs with the most
  significant keyphrases.

83
12

• To test feasibility of these ideas a prototype
  was developed.
• blindLight http//www.purl.org/NET/blindLight
• It receives a user-provided URL and produces
• A blindlighted version of the original URL.
• A list of keyphrases.
• An automatic summary.

92
13
Conclusions

• Proof-of-concept tests have been performed
• Details in the paper
• Results can be improved.
• Thorough study and analysis is needed.
• Really promising! ?
• Summary of the proposal
• Free text from just one document.
• Language independent (currently only western
  languages).
• Bio-inspired.
• Extremely simple to implement.

100
14
Merci beaucoup!
Muchas gracias!

• Thank you!