Hybrid Approach for Single Text Document Summarization using Statistical and Sentiment Features

Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. We are proposing a hybrid model for a single text document summarization. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures : sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. Our idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison we have generated five system summaries Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score.Comment: 20 pag

PerSum: Novel Systems for Document Summarization in Persian

In this paper we explore the problem of document summarization in Persian language from two distinct angles. In our first approach, we modify a popular and widely cited Persian document summarization framework to see how it works on a realistic corpus of news articles. Human evaluation on generated summaries shows that graph-based methods perform better than the modified systems. We carry this intuition forward in our second approach, and probe deeper into the nature of graph-based systems by designing several summarizers based on centrality measures. Ad hoc evaluation using ROUGE score on these summarizers suggests that there is a small class of centrality measures that perform better than three strong unsupervised baselines.Comment: 42 pages, 9 figure

Multi-Topic Multi-Document Summarizer

Current multi-document summarization systems can successfully extract summary sentences, however with many limitations including: low coverage, inaccurate extraction to important sentences, redundancy and poor coherence among the selected sentences. The present study introduces a new concept of centroid approach and reports new techniques for extracting summary sentences for multi-document. In both techniques keyphrases are used to weigh sentences and documents. The first summarization technique (Sen-Rich) prefers maximum richness sentences. While the second (Doc-Rich), prefers sentences from centroid document. To demonstrate the new summarization system application to extract summaries of Arabic documents we performed two experiments. First, we applied Rouge measure to compare the new techniques among systems presented at TAC2011. The results show that Sen-Rich outperformed all systems in ROUGE-S. Second, the system was applied to summarize multi-topic documents. Using human evaluators, the results show that Doc-Rich is the superior, where summary sentences characterized by extra coverage and more cohesion

Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Extract with Order for Coherent Multi-Document Summarization

In this work, we aim at developing an extractive summarizer in the multi-document setting. We implement a rank based sentence selection using continuous vector representations along with key-phrases. Furthermore, we propose a model to tackle summary coherence for increasing readability. We conduct experiments on the Document Understanding Conference (DUC) 2004 datasets using ROUGE toolkit. Our experiments demonstrate that the methods bring significant improvements over the state of the art methods in terms of informativity and coherence.Comment: TextGraphs-11 at ACL 201

Extractive Summarization using Deep Learning

This paper proposes a text summarization approach for factual reports using a deep learning model. This approach consists of three phases: feature extraction, feature enhancement, and summary generation, which work together to assimilate core information and generate a coherent, understandable summary. We are exploring various features to improve the set of sentences selected for the summary, and are using a Restricted Boltzmann Machine to enhance and abstract those features to improve resultant accuracy without losing any important information. The sentences are scored based on those enhanced features and an extractive summary is constructed. Experimentation carried out on several articles demonstrates the effectiveness of the proposed approach. Source code available at: https://github.com/vagisha-nidhi/TextSummarizerComment: Accepted to 18th International Conference on Computational Linguistics and Intelligent Text Processin

Toward Selectivity Based Keyword Extraction for Croatian News

Preliminary report on network based keyword extraction for Croatian is an unsupervised method for keyword extraction from the complex network. We build our approach with a new network measure the node selectivity, motivated by the research of the graph based centrality approaches. The node selectivity is defined as the average weight distribution on the links of the single node. We extract nodes (keyword candidates) based on the selectivity value. Furthermore, we expand extracted nodes to word-tuples ranked with the highest in/out selectivity values. Selectivity based extraction does not require linguistic knowledge while it is purely derived from statistical and structural information en-compassed in the source text which is reflected into the structure of the network. Obtained sets are evaluated on a manually annotated keywords: for the set of extracted keyword candidates average F1 score is 24,63%, and average F2 score is 21,19%; for the exacted words-tuples candidates average F1 score is 25,9% and average F2 score is 24,47%

Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues

Prosodic cues in conversational speech aid listeners in discerning a message. We investigate whether acoustic cues in spoken dialogue can be used to identify the importance of individual words to the meaning of a conversation turn. Individuals who are Deaf and Hard of Hearing often rely on real-time captions in live meetings. Word error rate, a traditional metric for evaluating automatic speech recognition, fails to capture that some words are more important for a system to transcribe correctly than others. We present and evaluate neural architectures that use acoustic features for 3-class word importance prediction. Our model performs competitively against state-of-the-art text-based word-importance prediction models, and it demonstrates particular benefits when operating on imperfect ASR output.Comment: 8 pages, 2 figure

Keyphrase Based Arabic Summarizer (KPAS)

This paper describes a computationally inexpensive and efficient generic summarization algorithm for Arabic texts. The algorithm belongs to extractive summarization family, which reduces the problem into representative sentences identification and extraction sub-problems. Important keyphrases of the document to be summarized are identified employing combinations of statistical and linguistic features. The sentence extraction algorithm exploits keyphrases as the primary attributes to rank a sentence. The present experimental work, demonstrates different techniques for achieving various summarization goals including: informative richness, coverage of both main and auxiliary topics, and keeping redundancy to a minimum. A scoring scheme is then adopted that balances between these summarization goals. To evaluate the resulted Arabic summaries with well-established systems, aligned English/Arabic texts are used through the experiments.Comment: INFOS 2012, The 8th INFOS2012 International Conference on Informatics and Systems, 14-16 May, 201

An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

The fast-growing amount of information on the Internet makes the research in automatic document summarization very urgent. It is an effective solution for information overload. Many approaches have been proposed based on different strategies, such as latent semantic analysis (LSA). However, LSA, when applied to document summarization, has some limitations which diminish its performance. In this work, we try to overcome these limitations by applying statistic and linear algebraic approaches combined with syntactic and semantic processing of text. First, the part of speech tagger is utilized to reduce the dimension of LSA. Then, the weight of the term in four adjacent sentences is added to the weighting schemes while calculating the input matrix to take into account the word order and the syntactic relations. In addition, a new LSA-based sentence selection algorithm is proposed, in which the term description is combined with sentence description for each topic which in turn makes the generated summary more informative and diverse. To ensure the effectiveness of the proposed LSA-based sentence selection algorithm, extensive experiment on Arabic and English are done. Four datasets are used to evaluate the new model, Linguistic Data Consortium (LDC) Arabic Newswire-a corpus, Essex Arabic Summaries Corpus (EASC), DUC2002, and Multilingual MSS 2015 dataset. Experimental results on the four datasets show the effectiveness of the proposed model on Arabic and English datasets. It performs comprehensively better compared to the state-of-the-art methods.Comment: This is a pre-print of an article published in Arabian Journal for Science and Engineering. The final authenticated version is available online at: https://doi.org/10.1007/s13369-018-3286-