This paper is the best one in the DataMining session in the ICPET 2018. Paper about: Graph-based Technique for Extracting Keyphrases in a single document (GTEK) is introduced. GTEK is based on the graph-based representation of text. GTEK motivated by: A phrase may be important if it appears in the most important sentences in the document. The Most important KP must cover all sub-topics of document. GTEK groups the sentences into graph-model clusters. Then ranks them using TextRank algorithm. Finally, the most frequent phrases in the high ranked sentences are selected as document keyphrases. Experimental results show that GTEK extracts the most keyphrases of two datasets.

1. Graph-Based Technique for Extracting Keyphrases
In a Single-Document (GTEK(
Mahmoud R. Alfarra
m.farra@cst.ps
Abdalfattah M. Alfarra
ab.alfarra@cst.ps
University College of Science and Technology

2. OutlineOutline
 Abstract
 GTEK’s Model
 What’s new in GTEK ?
 Experimental Results & Conclusions

3. Abstract
Graph-based Technique for Extracting Keyphrases in a single document
(GTEK) is introduced.
GTEK is based on the graph-based representation of text.
GTEK motivated by:
 A phrase may be important if it appears in the most important sentences in the
document.
 The Most important KP must cover all sub-topics of document.
GTEK groups the sentences into graph-model clusters.Then ranks them
usingTextRank algorithm.
Finally, the most frequent phrases in the high ranked sentences are selected
as document keyphrases.
Experimental results show that GTEK extracts the most keyphrases of two
datasets.

4. GTEK’s Model Doc
Construct one accumulative
graph using (DIG) model
Sentence Clustering using GSOM
Cl1 Cl2 Cl3
Cln. . .
N-Graph based Clusters
Sentence Ranking using TextRank
Cl1 Cl2 Cl3
Cln. . .
Ranked sentences in each cluster
The most important sentences in each cluster
Sent1 Sent2 Sentx. . .
The most frequent KP in the most important
from each cluster
KP1 KP2
KPi. . .
More KP ?

5. What’s new in GTEKWhat’s new in GTEK??
GTEK is based on the graph-based representation of text.
GTEK considers the impact of the sentence on the phrases
in a document.
GTEK ensures that the extracted keyphrases will cover all
main sub-topics based on a clustering-based.

6. Experimental Results & ConclusionsExperimental Results & Conclusions
Data Set No of documents Manually labeled KP
UCST 330 Yes
Hulth2003 1,460 Yes

7. Experimental Results & ConclusionsExperimental Results & Conclusions
Method Recall Precision F-measure Covering
GTEK 76.7 86.8 81.1 85.2%
Text Rank 48.6 50.0 49.2 44.3%
TF - IDF 34 33 33.5 32.7%
Results on UCST-news
dataset

8. Experimental Results & ConclusionsExperimental Results & Conclusions
Method Recall Precision F-measure Covering
GTEK 75.2 82.3 78.6 87.3%
Text Rank 40 41 40.5 48.2%
TF - IDF 32 31 31.5 34.9%
Results on UCST-news
dataset

9. Experimental Results & ConclusionsExperimental Results & Conclusions
The extracted keyphrases cover the most important
sentences and the main sub-topics in a document.
GTEK performs better than other baseline methods on two
datasets.
GTEK produces improved results compared withTextRank
andTF-IDF on two datasets.
As Future work, GTEK will be used to extract the KP of
multi-documents and generate the summarization of text.

10. Thanks a lotThanks a lot