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

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Presentation on theme: "Naive Algorithms for Key-phrase Extraction and Text Summarization from a Single Document inspired by the Protein Biosynthesis Process Daniel Gayo Avello."— Presentation transcript:

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 What’s the problem? Document reading is a time consuming task… Many common documents (e.g., , newsgroup posts, web pages) lack of abstract or keywords… But, they are “electronic” so we can work on them in some way…   8%

3 What’s 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?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…? Let’s 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 42% Our biological inspiration The protein biosynthesis process… DNA copied into a single-stranded mRNA molecule mRNA AUGUAA AUGCCGGGUUACUAA UAC Polypeptide chain Protein folded into a 3D structure Folding process Transcription Initiation Elongation Termination aminoacids Could we mimic this to distill from a single document keyphrases and summaries!?

7 The “ingredients”… Biological elementComputational “counterpart” tRNASpliced document “genome” mRNADocument’s plain text RibosomeAlgorithm Polypeptide chain Document chunks with significance weights ProteinKeyphrases 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.) The rain in Spain stays mainly in the plain. Original document … n -grams Relative frequency1.975 2.013 Fair Specific Mutual Information Assigning weights to n -grams 67%

10 Document genome translation The rain in Spain stays mainly in the plain. The- The-20 he-r he-r29 e-ra e-ra24 pseudo-mRNA 20The49 The r 73 The ra etc. 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.Result: Document spliced into “chunks” with maximum average significance. The rain in Spain stays mainly in the plain 75%

11 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. Folding the “protein” / summarization Work on Early Stage 83%

12 To test feasibility of these ideas a prototype was developed. 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 1.Free text from just one document. 2.Language independent (currently only western languages). 3.Bio-inspired. 4.Extremely simple to implement. 100%

14 Merci beaucoup!¡Muchas gracias!Thank you!


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