An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts

Mohamed, Muhidin; Oussalah, Mourad

doi:10.1109/bigdataservice.2016.31

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…The approach can also be employed for common open-class words, not necessarily named-entities, provided the existence of a Wikipedia entry. But such an approach has not been pursued in this paper, although one acknowledges other related works following such direction (Gabrilovich and Markovitch 1996;Taieb et al 2013;Mohamed and Oussalah 2016). The application of Eqs.…”

Section: Google Deprecated Its Local Api Access Since October 2013 Whmentioning

confidence: 98%

A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

Mohamed

Oussalah

2019

Lang Resources & Evaluation

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Section: Google Deprecated Its Local Api Access Since October 2013 Whmentioning

confidence: 98%

A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

Mohamed

Oussalah

2019

Lang Resources & Evaluation

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“…Muhidin Mohamed, Mourad Oussalah [8] proposed an innovative graph-based text summarization model for generic single and multi-document summarization. The approach involves four unique processing stages: parsing sentences semantically using Semantic Role Labeling (SRL), grouping semantic arguments while matching semantic roles to Wikipedia concepts, constructing a weighted semantic graph for each document and linking its sentences (nodes) through the semantic relatedness of the Wikipedia concepts.…”

Section: Related Workmentioning

confidence: 99%

Automatic summarization of Malayalam documents using clause identification method

Sunitha¹,

Jaya²,

Ganesh³

2019

IJECE

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<span>Text summarization is an active research area in the field of natural language processing. Huge amount of information in the internet necessitates the development of automatic summarization systems. There are two types of summarization techniques: Extractive and Abstractive. Extractive summarization selects important sentences from the text and produces summary as it is present in the original document. Abstractive summarization systems will provide a summary of the input text as is generated by human beings. Abstractive summary requires semantic analysis of text. Limited works have been carried out in the area of abstractive summarization in Indian languages especially in Malayalam. Only extractive summarization methods are proposed in Malayalam. In this paper, an abstractive summarization system for Malayalam documents using clause identification method is proposed. As part of this research work, a POS tagger and a morphological analyzer for Malayalam words in cricket domain are also developed. The clauses from input sentences are identified using a modified clause identification algorithm. The clauses are then semantically analyzed using an algorithm to identify semantic triples - subject, object and predicate. The score of each clause is then calculated by using feature extraction and the important clauses which are to be included in the summary are selected based on this score. Finally an algorithm is used to generate the sentences from the semantic triples of the selected clauses which is the abstractive summary of input documents.</span>

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“…On the other hand, data analytics is the technical process of transforming raw data into meaningful insights for better decision making. This includes text analytics, e.g., summarization, since unstructured text forms the highest percentage of current big data [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Trends of digitalization and adoption of big data & analytics among UK SMEs: Analysis and lessons drawn from a case study of 53 SMEs

Mohamed

Weber

2020

2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)

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Small and Medium Enterprises (SMEs) now generate digital data at an unprecedented rate from online transactions, social media marketing and associated customer interactions, online product/service reviews and feedback, clinical diagnosis, Internet of Things (IoT) sensors, and production processes. All these forms of data can be transformed into monetary value if put into a proper data value chain. This requires both skills and IT investments for the longterm benefit of businesses. However, such spending is beyond the capacity of most SMEs due to their limited resources and restricted access to finance. This paper presents lessons learned from a case study of 53 UK SMEs, mostly from the West Midlands region of England, supported as part of a 3-year ERDF 1 project -Big Data Corridor 2 -in the areas of big data management, analytics and related IT issues. Based on our study's sample companies, several perspectives including digital technology trends, challenges facing the UK SMEs, and the state of their adoption in data analytics and big data, are presented.

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An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts

Cited by 10 publications

References 9 publications

A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

Automatic summarization of Malayalam documents using clause identification method

Trends of digitalization and adoption of big data & analytics among UK SMEs: Analysis and lessons drawn from a case study of 53 SMEs

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