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  • 1
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    Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization (2020)
    Hindawi
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    Publication Date: 2020-08-25
    Description: Huge data on the web come from discussion forums, which contain millions of threads. Discussion threads are a valuable source of knowledge for Internet users, as they have information about numerous topics. The discussion thread related to single topic comprises a huge number of reply posts, which makes it hard for the forum users to scan all the replies and determine the most relevant replies in the thread. At the same time, it is also hard for the forum users to manually summarize the bulk of reply posts in order to get the gist of discussion thread. Thus, automatically extracting the most relevant replies from discussion thread and combining them to form a summary are a challenging task. With this motivation behind, this study has proposed a sentence embedding based clustering approach for discussion thread summarization. The proposed approach works in the following fashion: At first, word2vec model is employed to represent reply sentences in the discussion thread through sentence embeddings/sentence vectors. Next, K-medoid clustering algorithm is applied to group semantically similar reply sentences in order to reduce the overlapping reply sentences. Finally, different quality text features are utilized to rank the reply sentences in different clusters, and then the high-ranked reply sentences are picked out from all clusters to form the thread summary. Two standard forum datasets are used to assess the effectiveness of the suggested approach. Empirical results confirm that the proposed sentence based clustering approach performed superior in comparison to other summarization methods in the context of mean precision, recall, and F-measure.
    Print ISSN: 1076-2787
    Electronic ISSN: 1099-0526
    Topics: Computer Science , Mathematics
    Published by Hindawi
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  • 2
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    Nonlinear electrostatic solitary pulses in magnetized quantum plasma with relative density effects of spin-up and spin-down electrons (2016)
    American Institute of Physics (AIP)
    Details
    Publication Date: 2016-11-15
    Description: A separated spin evolution quantum hydrodynamics model is employed to study low frequency electrostatic waves in plasmas having inertia-less degenerate electrons with spin-up n e ↑ and spin-down n e ↓ states and inertial classical ions. A two-dimensional plasma geometry is assumed having a uniform magnetic field, directed along the z-axis, i.e., B = B 0 z ̂ . A Zakharov-Kuznetsov (ZK) type equation is derived for the electrostatic potential via the Reductive Perturbation Technique. The parametric role of the spin density polarization ratio κ in the characteristics of solitary wave structures is investigated. We have observed that both the amplitude and width of the soliton are significantly affected by the spin polarization but the amplitude remains largely un-affected by variation in the magnetic field strength. We have also carried out pulse stability analysis and have found that the pulse soliton solution of the ZK equation is unstable to oblique perturbations. The dependence of the instability growth rate on the density polarization ratio κ along with other significant plasma parameters is traced analytically. We have shown that the first order growth rate of the instability decreases with an increase in the angle between the transverse component of the perturbation and the direction of the magnetic field, in the range ( 0 ≤ θ 〈 37.8 ° ) . We have also observed that the spin polarization affects the growth and increases as we move from the strongly spin-polarized plasma to a zero polarization case.
    Print ISSN: 1070-664X
    Electronic ISSN: 1089-7674
    Topics: Physics
    Published by American Institute of Physics (AIP)
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  • 3
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    Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics (2020)
    Hindawi
    Details
    Publication Date: 2020-08-01
    Description: Information is exploding on the web at exponential pace, so online movie review is becoming a substantial information resource for online users. However, users post millions of movie reviews on regular basis, and it is not possible for users to summarize the reviews. Movie review classification and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is demanded to summarize the vast amount of movie reviews, and it will allow the users to speedily distinguish the positive and negative aspects of a movie. This study has proposed an approach for movie review classification and summarization. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents, and represent the review documents as a vector space model. Next, the Naïve Bayes algorithm is employed to classify the movie reviews (represented as a feature vector) into positive and negative reviews. For the task of movie review summarization, Word2vec feature extraction technique is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are used to calculate the salience score of each review sentence in clusters. Finally, the top-ranked sentences are chosen based on highest salience scores to produce the extractive summary of movie reviews. Experimental results reveal that the proposed machine learning approach is superior than other state-of-the-art approaches.
    Print ISSN: 1058-9244
    Electronic ISSN: 1875-919X
    Topics: Computer Science , Media Resources and Communication Sciences, Journalism
    Published by Hindawi
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