The task of keyword extraction is to automatically identify a set of terms that best describe the document. Automatic keyword extraction establishes a foundation for various natural language processing applications: information retrieval, the automatic indexing and classification of documents, automatic summarization and high-level semantic description, etc. Although the keyword extraction applications usually work on single documents (document-oriented task), keyword extraction is also applicable to a more demanding task, i.e. the keyword extraction from a whole collection of documents or from an entire web site, or from tweets from Twitter. In the era of big-data, obtaining an effective and efficient method for automatic keyword extraction from huge amounts of multi-topic textual sources is of high importance. We proposed a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction. Testing of the portability of the SBKE was tested on Croatian, Serbian and English texts – more precisely it was developed on Croatian News and ported for extraction from parallel abstracts of scientific publication in the Serbian and English languages. The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.

1. Department of Informatics, University of Rijeka
Radmile Matejčić 2, 51000 Rijeka, Croatia
Tel.: + 385 51 584 700 Fax: + 385 51 584 749
www.langnet.uniri.hr
Extracting keywords
from texts
Sanda Martinčić-Ipšić
smarti@uniri.hr
langnet.uniri.hr
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2. Introduction 1/4
Keyword extraction
automatically identify a that best describe the
document
the most representative features (terms) of the source text
huge amount of multi topic textual sources
applications:
summarization, indexing, labeling, categorization, clustering
document-oriented task (single documents)
collection-oriented task (i.e. entire web site)
more demanding
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3. 2
How KE works?
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4. Introduction 2/4
keyword extraction is traditionally
based on statistical methods
learning from
or graph, since the number of words in isolated documents is limited
the source (document, text) is modelled in a network
words are nodes and their co-occurrence is represented with links
links can model different linguistic relations (syntactic; semantic)
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directed and weighted co-occurrence network for two
sentences:
“Selectivity-based keyword extraction method is fully
unsupervised. It consists of two phases: keyword extraction
and keyword expansion.”
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5. Introduction 3/4
can exploit:
or level measures:
coreness, clustering coefficient
PageRank motivated ranking score or
HITS motivated hub and authority score
communities
strength, centrality
and eigenvector
centrality
the average strength of the node
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S. Beliga, A. Meštrović, S. Martinčić-Ipšić. "An Overview of Graph-Based
Keyword Extraction Methods and Approaches". Journal of Information
and Organizational Sciences, 39(1), pp. 1-20, 2015.

6. Complex network analysis 1/2
Directed and weighted network [Newman, 2010]
N – number of nodes
M – number of links
wij – weight for link which connects nodes i and j
Ki – node degree: number of links incident upon a node
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𝒅𝒄𝒊
(𝒊𝒏/𝒐𝒖𝒕)
=
𝒌𝒊
(𝒊𝒏/𝒐𝒖𝒕)
𝑵 − 𝟏
𝒄𝒄𝒊 =
𝑵 − 𝟏
σ𝒊≠𝒋 𝒅𝒊𝒋
𝒃𝒄𝒊 =
σ𝒊≠𝒋≠𝒌
𝝈𝒋𝒌(𝒊)
𝝈𝒋𝒌
(𝑵 − 𝟏)(𝑵 − 𝟐)
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7. 3. Complex network analysis 1/2
strength and selectivity
selectivity
average weight distribution on the links of the single node
generalized selectivity
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𝒔𝒊
𝒊𝒏/𝒐𝒖𝒕
= ෍
𝒋
𝒘𝒋𝒊/𝒊𝒋
𝒈𝒆𝒊
α 𝒊𝒏/𝒐𝒖𝒕
= 𝒌𝒊
𝒊𝒏/𝒐𝒖𝒕 𝒔𝒊
𝒊𝒏/𝒐𝒖𝒕
𝒌𝒊
𝒊𝒏/𝒐𝒖𝒕
α
[Masucci &
Rodgers, 2009]𝒆𝒊
𝒊𝒏/𝒐𝒖𝒕
=
𝒔𝒊
𝒊𝒏/𝒐𝒖𝒕
𝒌𝒊
𝒊𝒏/𝒐𝒖𝒕
adapted from [Opsahl et al., 2010] in
[Beliga, Meštrović, Martinčić-Ipšić, 2016]
α∈ [0,1] – prefers nodes with higher
degrees
α ∈ [1, +∞] – prefers nodes with
lower degrees
𝛼 = 1– node strength
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8. Data: Croatian and English texts
collections with manually annotated keywords by
human experts
HINA Croatian News Agency
Wikipedia: technical reports covering different aspects of CS
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HINA WIKI-20
8 human experts 15 teams (2 undergrad. stud.)
60 texts 20 texts
Croatian English
10 keywords on AVG per human 5,7 keywords on AVG per team
human consistency≈ 39.5% team consistency ≈ 30.5%
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9. Network construction
network per document
from all documents (collection extraction)
each word
two words are linked if they are adjacent in the sentence
proportional to the overall co-occurrence frequencies of
the corresponding word pairs
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10. Comparison of centrality measures
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DATA
SET
MEASURE
TOP 5 TOP 10
Ravg Pavg F1avg Ravg Pavg F1avg
HINA
Closeness: 𝑐𝑐𝑖 4.3 39.3 7.2 8.4 42.9 12.8
Betweenness: 𝑏𝑐𝑖 3.6 40.8 6.5 9.2 52.8 13.9
in/out-degree: 𝑑𝑐𝑖
𝑖𝑛/𝑜𝑢𝑡 23.0 38.4 17.0 62.1 25.1 26.6
in/out-selectivity: 𝑒𝑖
𝑖𝑛/𝑜𝑢𝑡 14.0 35.9 19.3 16.8 38.0 22.1
WIKI-20
Closeness: 𝑐𝑐𝑖 1.6 42.5 3.1 5.3 63.3 9.7
Betweenness: 𝑏𝑐𝑖 0.3 10.0 0.5 2.6 55.0 4.9
in/out-degree: 𝑑𝑐𝑖
𝑖𝑛/𝑜𝑢𝑡 0.1 5.0 0.3 2.5 57.5 4.8
in/out-selectivity: 𝑒𝑖
𝑖𝑛/𝑜𝑢𝑡 2.3 17.8 3.9 8.6 18.2 10.8
TOP 10 ranked words:
in-degree: to be, and, in, on, which, for, but, this, self, of
betweenness: to be, and, in, on, self, this, which, for, Croatian, but
in/out selectivity: Bratislava, area, Tuesday, inland, revolution, verification, decade, Balkan,
freedom, Universe Data Science 4.0 Conference

11. Selectivity
differentiate between two types of nodes (words)
high strength and high degree values
: stop-words, conjunctions, prepositions
high strength and low degree
: nouns, adjectives, verbs and
words that are part of collocations, keyphrases, names, etc.
efficiently
extract
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12. SBKE method
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13. SBKE method
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14. 14
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15. SBKE with Generalized Selectivity
SBKE method can be easily adjusted with new
measures
generalized selectivity measure: 𝑔𝑒𝑖
α 𝑖𝑛/𝑜𝑢𝑡
α adjust the relationship between the node degree and strength –
provide different set of keywords
Find the α parameter which best fits the experimental settings
according to 𝐼𝐼𝐶 𝑂 scores
tuning to the data set
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16. SBKE with Generalized Selectivity
The performance of the first step (SET1) of the SBKE method in terms of 𝐼𝐼𝐶 𝑂 scores for the
TOP 5 and TOP 10 keywords measured for generalized selectivity 𝑔𝑒𝑖
α 𝑖𝑛/𝑜𝑢𝑡
with different
values of parameter α (plotted as lines) and selectivity (𝑒
𝑖𝑛/𝑜𝑢𝑡
) values (plotted as dots on the
y-axis) for HINA and WIKI-20 datasets
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17. Document vs. Collection-Oriented
Extraction Results
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HINA
60 individual networks integral network
Ravg Pavg F1avg R P F1
SET1 18.90 39.35 23.60 30.71 35.80 33.06
SET2 19.74 39.15 24.76 33.46 33.97 33.71
SET3 29.55 22.96 22.71 60.47 19.89 28.89
WIKI-
20
20 individual
networks
(ei
in/out >1)
integral network
( ei
in/out >1)
integral network
( ei
in/out >2)
Ravg Pavg F1avg R P F1 R P F1
SET1 59.06 13.40 21.48 76.17 19.08 30.51 32.71 30.30 31.46
SET2 60.12 12.33 20.46 76.64 18.87 30.29 36.45 31.97 34.06
SET3 62.17 12.05 20.19 77.50 15.75 26.18 36.68 32.04 34.21
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18. Keyword Extraction from Parallel
Abstracts
scientific journal “Underground Mining Engineering”
published by the University of Belgrade, Faculty of Mining and Geology
papers from diffrent fields of mining, geology, and geosciences (2002-2014)
bilinguall papers written in Serbian and in English
available online as aligned parallel text in the
Biblisha digital library: http://jerteh.rs/biblisha/ListaDokumenata.aspx?JCID=2&lng=en
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19. Data
Experiment
collection of 50 bilingual documents
with approximately 4,800 aligned sentences
originally written in Serbian and then translated into English by professionals
translators
Bilingual Serbian-English KE dataset introduced in this paper is
publicly available from http://langnet.uniri.hr/resources.html
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S. Beliga, Kitanović, O., Stanković, R., S. Martinčić-Ipšić.
"Keyword Extraction from Parallel Abstracts of Scientific
Publications".
Semantic Keyword-Based Search on Structured Data Sources,
LNCS 10546, Springer, Gdańsk, Poland, pp. 44-45, 2018.

20. Results 1/2
Evaluation according to the
set of annotated keywords; provided by the author(s) of the abstracts
set of annotated keywords without OOV words
𝑅, 𝑃 and 𝐹1 score achieves higher values for KW without OOV words
this is expected because people tagged KW that did not apear in texts
~18% of the KWs in English
~19% of the KWs in Serbian
all scores better for Serbian than for English 20
~3%
OOV
Previous research:
holds for
Croatian language.
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21. Results 2/2
Open questions??:
performs better on longer texts??
containing more information on the structural properties of the
input text
performs better for morphologically rich languages??
Italian ?? 21
Language Text type #words
(AVG)
F1 score
English Wikipedia
articles
5,919 24,8%
Croatian Newspap
er articles
335 34,21%
English Parallel
abstracts
of SP
110 46,73%
Serbian 100 49,57%
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SBKE according to text size
SBKE has been tested on
longer English Wikipedia texts
shorter Croatian newspaper article
short Serbian and English abstracts
SBKE in this study
can be applied to shorter documents
portable to new domain (geology)

22. SBKE Conclusion 1/2
Selectivity-Based Keyword Extraction - SBKE
purely from statistical and structural information
the source text is reflected into the structure of the network
comparable with existing methods
low precision vs. high recall
beside keywords, personal names and entities - not marked as keywords
for document and for collection extraction tasks
Keyword annotation is highly subjective task
even human experts have difficulties to agree upon keyphrases
inter-annotator consistency (agreement) (IIC):
Croatian HINA 40% (SBKE 26%)
English Wiki-20 30% (SBKE 17%)
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23. SBKE Conclusion 2/2
SBKE method
does not require linguistic knowledge (from statistical and structural
information )
main advantages:
networks constructed from co-occurrence of words in the input texts
the method achieves results which are above the TFxIDF but below
humans
documet or collection oriented tasks
preprocessing is not necessary (works without stemming/lemmatization)
easily portable to new languages
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S. Beliga, A. Meštrović, S. Martinčić-Ipšić.
”Selectivity-Based Keyword Extraction Method”, International Journal on
Semantic Web and Information Systems, vol. 12, No. 3, pp. 1-26, 2016.

24. more @ langnet.uniri.hr
• Text genres differentiation
• Can we determine text genre using only network structure properties?
• Legislation or literature? Blogs vs. literature?
• Authorship attribution and language detection
• Text classification
• dimensionality reduction in BoW model using structural properties of
text
• Wikipedia
• knowledge extraction and linking concepts
• Twitter
• polarity (positive/negative) detection
• prediction of missing links
• Social networks
• Coautorship networks analyis
• scientometrics 24
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25. LangNet team: langnet.uniri.hr/members.html
Department of informatics:
Sanda Martinčić-Ipšić,
Ana Meštrović,
Slobodan Beliga,
Lucia Načinović Prskalo
Collaborators:
Tajana Ban Kirigin,
Mihaela Matešić,
Benedikt Perak,
Zoran Levnajić
Past members:
Tanja Miličić, Domagoj Margan, Edvin Močibob, Neven Matas, Kristina Ban, Hana Rizvić, Sabina Šišović,
Luka Vretenar
25* University of Rijeka grant 13.13.2.2.07
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26. Sanda Martinčić-Ipšić
smarti@uniri.hr
langnet.uniri.hr
?