Multi-document Text Summarization Research Articles

A knowledge-graph based text summarization scheme for mobile edge computing

As the demand for edge services intensifies, text, being the most common type of data, has seen a significant expansion in data volume and an escalation in processing complexity. Furthermore, mobile edge computing (MEC) service systems often faces challenges such as limited computational capabilities and difficulties in data integration, requiring the development and implementation of more efficient and lightweight methodologies for text data processing. To swiftly extract and analysis vital information from MEC text data, an automatic generation scheme of multi-document text summarization based on knowledge graph is proposed in this paper, named KGCPN. For the text data from MEC devices and applications, the natural language processing technology is used to execute the data pre-processing steps, which transforms the MEC text data into a computationally tractable and semantically comprehensible format. Then, the knowledge graph of multi-document text is constructed by integrating the relationship paths and entity descriptions. The nodes and edges of the knowledge graph serve to symbolize the semantic relationships within the text, and the Graph Convolution Neural network (GCN) is used to understand the text and learn the semantic representation. Finally, a pointer-generator network model accepts the encoding information from GCN and automatically generate a general text summarization. The experimental results indicate that our scheme can effectively facilitate the smart pre-processing and integration of MEC data.

Generic and Update Multi-Document Text Summarization based on Genetic Algorithm

In this paper, we addressed the generic and update text summarization tasks of a set of documents as a combinatorial optimization problem through a genetic algorithm and unsupervised textual features. Particularly under the news domain, input documents are a set of articles of varying sizes covering the same event. The main advantage of the proposed method is that it is language-independent. The experimental results demonstrated that the method performs well for both kinds of summarization. Moreover, we calculated the heuristics for update text summarization like a benchmark to compare state-of-the-art methods.

Personalized Multi-document Text Summarization using Deep Learning Techniques

The summary of a large piece of text or a document is a concise description of the same thing. It must retain most of the important points from the document and remove any kind of verbosity. The text summarization task is an information processing scheme that given a document or a set of documents, mines the most essential content from the source by considering the user or task at hand and presents the summary in a well-formed and brief text. Summarization can be done not just on one document, it can be done on a set of documents as well, which is called multi-document summarization. The proposed approach considers and in-corporates user preferences for the summarization task. Depending on the current user or current task, the summary could differ essentially. For an instance, a summary on ‘photosynthesis’, would be very different for the kid in 4th Grade vs. a student doing a bachelor's in Science. Therefore, while writing a summary, the reading history of the user is employed as a source of personalizing text summarization tasks. The proposed method is experimentally evaluated in the domain of news articles and obtained better summaries capable of extracting important concepts based on user preferences explained in the document when considering the relevant domain terms in the process of multi-document text summarization.

Weighted Graph Embedding Feature with Bi-Directional Long Short-Term Memory Classifier for Multi-Document Text Summarization

In this digital era, there is a tremendous increase in the volume of data, which adds difficulties to the person who utilizes particular applications, such as websites, email, and news. Text summarization targets to reduce the complexity of obtaining statistics from the websites as it compresses the textual document to a short summary without affecting the relevant information. The crucial step in multi-document summarization is obtaining a relationship between the cross-sentence. However, the conventional methods fail to determine the inter-sentence relationship, especially in long documents. This research develops a graph-based neural network to attain an inter-sentence relationship. The significant step in the proposed multi-document text summarization model is forming the weighted graph embedding features. Furthermore, the weighted graph embedding features are utilized to evaluate the relationship between the document’s words and sentences. Finally, the bidirectional long short-term memory (BiLSTM) classifier is utilized to summarize the multi-document text summarization. The experimental analysis uses the three standard datasets, the Daily Mail dataset, Document Understanding Conference (DUC) 2002, and Document Understanding Conference (DUC) 2004 dataset. The experimental outcome demonstrates that the proposed weighted graph embedding feature + BiLSTM model exceeds all the conventional methods with Precision, Recall, and F1 score of 0.5352, 0.6296, and 0.5429, respectively.

Binary Particle Swarm Optimization with an improved genetic algorithm to solve multi-document text summarization problem of Hindi documents

Automatic text summarization plays a vital role in text retrieval. However, very little interest, as well as attention to Hindi text summarization and the wide use of hybridization of an evolutionary and swarm-based method for solving optimization problems, motivates us to develop a technique for achieving better results. We have used a combination of the Title feature, Sentence length, Sentence position, Numerical Data, Thematic word, Proper Noun and Term frequency & Inverse Sentence Frequency to find the results. The proposed BPSO-IGA comprising Binary Particle Swarm Optimization (BPSO) and an improved genetic algorithm finds a well-formatted summary from multiple documents. The method for calculating the score of the sentence is proposed initially. A combination of BPSO and IGA finds the features’ optimal scores and then is used to generate the final summary. The proposed method is compared with six well-known techniques, SummaRuNNer, ChunkRank, TGraph, UniRank, FF-SE, and MDS-ACS, on FIRE 2011 Hindi dataset. To strengthen the hypothesis that all seven features are required in the summary generation, the summary is also generated using only ONE and a combination of more than one feature. It is found that the best summary is generated when all seven features are considered. The BPSO-IGA performs best among all the methods for precision, recall, f-measure, cohesion, and non-redundancy. This research aims to design a multi-document extractive text summarizer for Hindi documents using a swarm intelligence and evolutionary algorithm-based hybrid approach.

VoteSumm: A Multi-Document Summarization Scheme Using Influential Nodes of Multilayer Weighted Sentence Network

This work proposes a sentence network-based approach for performing the task of multi-document text summarization. The sentences of the input set of documents are represented by the nodes of the network. Weighted edges are added between the nodes to represent the semantic similarity between the corresponding sentences. The network has a multilayer structure, where each layer corresponds to an individual input document. This helps in effective differentiation between the inter-document and intra-document edges. A hyperparameter, namely layering factor, has been used to alter the strength of inter-document connections through reinforcement or weakening. It is hypothesized that the summary sentence nodes must act as effective information spreaders in the sentence network. Summary generation is performed by identifying the influential nodes of the network using VoteRank scheme. A comparative study with different network measures, such as Weighted Degree, PageRank, Betweenness centrality, and Closeness centrality reveals the efficacy of the proposed VoteSumm technique for multi-document text summarization. Improved performance is observed when an additional pre-processing step of syntactic simplification is applied on the raw text. Performance is further improved when keyword information is included in the simplified texts.

Hybrid multi-document text summarization via categorization based on BERT deep learning models

Text summarization is the process of employing a system to shorten a document or a collection of documents into brief paragraphs or sentences using various approaches. This paper presents text categorization using BERT to improve summarization task which is a state-of-the-art deep learning language processing model that performs significantly better than all other previous language models. Multi-document summarization (MDS) has got its bottleneck due to lack of training data and varied categories of documents. Aiming in this direction, the proposed novel hybrid summarization B-HEATS (Bert based Hybrid Extractive Abstractive Text Summarization)framework is a combination of extractive summary via categorization and abstractive summary using deep learning architecture RNN-LSTM-CNN to fine-tune BERT which results in the qualitative summary for multiple documents and overcomes out of vocabulary (OOV). The output layer of BERT is replaced using RNN-LSTM-CNN architecture to fine tune which improves the summarization model. The proposed automatic text summarization is compared over the existing models in terms of performance measures like ROUGE metrics achieves high scores as R1 score 43.61, R2 score 22.64, R3 score 44.95 and RL score is 44.27 on Benchmark DUC datasets.

Query-focused multi-document text summarization using fuzzy inference

The present paper proposes a fuzzy inference system for query-focused multi-document text summarization (MTS). The overall scheme is based on Mamdani Inferencing scheme which helps in designing Fuzzy Rule base for inferencing about the decision variable from a set of antecedent variables. The antecedent variables chosen for the task are from linguistic and positional heuristics, and similarity of the documents with the user-defined query. The decision variable is the rank of the sentences as decided by the rules. The final summary is generated by solving an Integer Linear Programming problem. For abstraction coreference resolution is applied on the input sentences in the pre-processing step. Although designed on the basis of a small set of antecedent variables the results are very promising.

Extractive summarization of Malayalam documents using latent Dirichlet allocation: An experience

Abstract Automatic text summarization (ATS) extracts information from a source text and presents it to the user in a condensed form while preserving its primary content. Many text summarization approaches have been investigated in the literature for highly resourced languages. At the same time, ATS is a complicated and challenging task for under-resourced languages like Malayalam. The lack of a standard corpus and enough processing tools are challenges when it comes to language processing. In the absence of a standard corpus, we have developed a dataset consisting of Malayalam news articles. This article proposes an extractive topic modeling-based multi-document text summarization approach for Malayalam news documents. We first cluster the contents based on latent topics identified using the latent Dirichlet allocation topic modeling technique. Then by adopting vector space model, the topic vector and sentence vector of the given document are generated. According to the relevant status value, sentences are ranked between the document’s topic and sentence vectors. The summary obtained is optimized for non-redundancy. Evaluation results on Malayalam news articles show that the summary generated by the proposed method is closer to the human-generated summaries than the existing text summarization methods.

A multi-objective memetic algorithm for query-oriented text summarization: Medicine texts as a case study

Automatic text summarization is a topic of great interest in many fields of knowledge. Particularly, query-oriented extractive multi-document text summarization methods have increased their importance recently, since they can automatically generate a summary according to a query given by the user. One way to address this problem is by multi-objective optimization approaches. In this paper, a memetic algorithm, specifically a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) has been developed, implemented, and applied to solve the query-oriented extractive multi-document text summarization problem. Experiments have been conducted with datasets from Text Analysis Conference (TAC), and the obtained results have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results have shown that the proposed approach has achieved important improvements with respect to the works of scientific literature. Specifically, 25.41%, 7.13%, and 30.22% of percentage improvements in ROUGE-1, ROUGE-2, and ROUGE-SU4 scores have been respectively reached. In addition, MOSFLA has been applied to medicine texts from the Topically Diverse Query Focus Summarization (TD-QFS) dataset as a case study.

Extractive Multi-document Text Summarization Leveraging Hybrid Semantic Similarity Measures

Because of the massive amount of textual information accessible today, automated extraction text summarization is one of the most extensively used ways to organize the information. The summarization mechanisms help to extract the important topics of data from a given set of documents. Extractive summarization is one method for providing a representative summary of a text by choosing the most pertinent sentences from the original text. Extractive multi-document text summarization systems' primary goal is to decrease the quantity of textual information in a document collection by concentrating on the most crucial subjects and removing irrelevant material. In the previous research, there are several methods such as term-weighting schemes and similarity metrics used for constructing an automated summary system. There are few studies that look at the performance of combining various Semantic similarity and word weighting techniques in automatic text summarization. We evaluated numerous semantic similarity metrics in extractive multi-document text summarization in this research. In the extractive multi-document text summarization discussed in this research, we looked at numerous semantic similarity metrics. ROUGE metrics have been used to evaluate the model performance in experiments using DUC datasets. Even more, the combination formed by different semantic similarity measures obtained the highest results in comparison with the other models.

Sentiment-oriented query-focused text summarization addressed with a multi-objective optimization approach

Nowadays, the automatic text summarization is a highly relevant task in many contexts. In particular, query-focused summarization consists of generating a summary from one or multiple documents according to a query given by the user. Additionally, sentiment analysis and opinion mining analyze the polarity of the opinions contained in texts. These two issues are integrated in an approach to produce an opinionated summary according to the user’s query. Thereby, the query-focused sentiment-oriented extractive multi-document text summarization problem entails the optimization of different criteria, specifically, query relevance, redundancy reduction, and sentiment relevance. An adaptation of the metaheuristic population-based crow search algorithm has been designed, implemented, and tested to solve this multi-objective problem. Experiments have been carried out by using datasets from the Text Analysis Conference (TAC) datasets. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics and the Pearson correlation coefficient have been used for the performance assessment. The results have reported that the proposed approach outperforms the existing methods in the scientific literature, with a percentage improvement of 75.5% for ROUGE-1 score and 441.3% for ROUGE-2 score. It also has been obtained a Pearson correlation coefficient of +0.841, reporting a strong linear positive correlation between the sentiment scores of the generated summaries and the sentiment scores of the queries of the topics.

An interactive query-based approach for summarizing scientific documents

PurposeQuery-based summarization approaches might not be able to provide summaries compatible with the user’s information need, as they mostly rely on a limited source of information, usually represented as a single query by the user. This issue becomes even more challenging when dealing with scientific documents, as they contain more specific subject-related terms, while the user may not be able to express his/her specific information need in a query with limited terms. This study aims to propose an interactive multi-document text summarization approach that generates an eligible summary that is more compatible with the user’s information need. This approach allows the user to interactively specify the composition of a multi-document summary.Design/methodology/approachThis approach exploits the user’s opinion in two stages. The initial query is refined by user-selected keywords/keyphrases and complete sentences extracted from the set of retrieved documents. It is followed by a novel method for sentence expansion using the genetic algorithm, and ranking the final set of sentences using the maximal marginal relevance method. Basically, for implementation, the Web of Science data set in the artificial intelligence (AI) category is considered.FindingsThe proposed approach receives feedback from the user in terms of favorable keywords and sentences. The feedback eventually improves the summary as the end. To assess the performance of the proposed system, this paper has asked 45 users who were graduate students in the field of AI to fill out a questionnaire. The quality of the final summary has been also evaluated from the user’s perspective and information redundancy. It has been investigated that the proposed approach leads to higher degrees of user satisfaction compared to the ones with no or only one step of the interaction.Originality/valueThe interactive summarization approach goes beyond the initial user’s query, while it includes the user’s preferred keywords/keyphrases and sentences through a systematic interaction. With respect to these interactions, the system gives the user a more clear idea of the information he/she is looking for and consequently adjusting the final result to the ultimate information need. Such interaction allows the summarization system to achieve a comprehensive understanding of the user’s information needs while expanding context-based knowledge and guiding the user toward his/her information journey.

Multi-document extractive text summarization based on firefly algorithm

Extracting relevant information from a large amount of data is a challenging task. Automatic text summarization is a potential solution for obtaining this information. In this paper, a nature inspired swarm intelligence-based algorithm viz. firefly algorithm for multi-document text summarization is proposed. A new fitness function consisting of three features viz. topic relation factor, cohesion factor and readability factor is utilized. The experiments are performed on datasets from Document Understanding Conference i.e. DUC-2002, DUC-2003 and DUC-2004. The performance of the algorithm has been evaluated using ROUGE score. The performance of the proposed algorithm is compared with some other nature inspired ones such as particle swarm optimization (PSO) and genetic algorithm (GA). The performance of the proposed algorithm outperforms the other adopted ones.

Extractive multi-document text summarization using dolphin swarm optimization approach

Nowadays, extracting the desired information from internet source is a challenging task because of a large amount of information available on the internet. So, we propose a new extractive based approach for multi-document text summarization to extract useful information from multi-document. Initially, the redundant contents in the document create a single text file from the multiple text file document. The content coverage and non-redundancy features are achieved by Word Mover Distance (WMD) and Modified Normalized Google Distance (M-NGD) (WM) Hybrid Weight Method based similarity approaches. For feature weight optimization, we use the Dolphin swarm optimization (DSO) which is a metaheuristic approach. The proposed approach is tested under python with multiling 2013 dataset and the performances have been evaluated with ROUGE and AutoSummENG metrics. The investigational outcomes show that the proposed technique works well and very much effective for multi-document text summarization.

A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA

The explosive growth of textual data on the web and the problem of obtaining desired information through this enormous volume of data has led to a dramatic increase in demand for developing automatic text summarization systems. For this reason, this paper presents a novel multi-document text summarization approach, called MTSQIGA, which extracts salient sentences from source document collection to generate the summary. The proposed generic summarizer models extractive summarization as a binary optimization problem that applies a modified quantum-inspired genetic algorithm (QIGA) in its processing stage to find the best solution. Objective function of our approach plays an important role in optimizing linear combination of coverage, relevance, and redundancy factors which consists of six sentence scoring measures. To ensures the generation of a summary with predefined length limit, the presented QIGA employs a modified quantum measurement and a self-adaptive quantum rotation gate based on the quality and length of the summary. Evaluation of the proposed system was performed on DUC 2005 and 2007 benchmark datasets in terms of ROUGE standard measures. Comparison of MTSQIGA with existing state-of-the-art approaches for multi-document summarization shows superior performance of the proposed systems over other methods on both existing benchmark datasets. It also indicates promising efficiency of our proposed algorithm on applying quantum-inspired genetic algorithm to the text summarization tasks.

The impact of term-weighting schemes and similarity measures on extractive multi-document text summarization

Automatic text summarization is currently a topic of great interest in many knowledge fields. Extractive multi-document text summarization methods aim to reduce the textual information from a document collection by covering the main content and reducing the redundant information. In the scientific literature, there are different approaches related to term-weighting schemes and similarity measures, which are necessary for implementing an automatic summary system. However, to the best of the authors’ knowledge, there are no studies to analyze the performance of the different schemes and measures. In this paper, all possible combinations of the most common term-weighting schemes and similarity measures used in the extractive multi-document text summarization field have been implemented, compared, and analyzed. Experiments have been performed with Document Understanding Conferences (DUC) datasets, and the model performance has been assessed with eight Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics and the execution time. Results show that the best term-weighting scheme is the term-frequency inverse-sentence-frequency scheme, and the best similarity measure is the cosine similarity. Even more, the combination formed by both of them has obtained the best average results in 87.5% of ROUGE scores compared to the other combinations.

Topic modeling combined with classification technique for extractive multi-document text summarization

The qualities of human readable summaries available in the datasets are not up to the mark, leading to issues in creating an accurate model for text summarization. Although recent works have been largely built upon this issue and set up a strong platform for further improvements, they still have many limitations. Looking in this direction, the paper proposes a novel methodology for summarizing a corpus of documents to generate a coherent summary using topic modeling and classification technique. The objectives of the propose work are highlighted below: The outcomes of the empirical work show that the proposed model is more promising compared to the well-known text summarization models.

Unsupervised extractive multi-document text summarization using a genetic algorithm

The task of Extractive Multi-Document Text Summarization (EMDTS) aims at building a short summary with essential information from a collection of documents. In this paper, we propose an EMDTS method using a Genetic Algorithm (GA). The fitness function considering two unsupervised text features: sentence position and coverage. We propose the binary coding representation, selection, crossover, and mutation operators. We test the proposed method on the DUC01 and DUC02 data set, four different tasks (summary lengths 200 and 400 words), for each of the collections of documents (in total, 876 documents) are tested. Besides, we analyze the most frequently used methodologies to summarization. Moreover, different heuristics such as topline, baseline, baseline-random, and lead baseline are calculated. In the results, the proposed method achieves to improve the state-of-art results.

Modern Multi-Document Text Summarization Techniques

Text Summarization is the technique in which the source document is simplified, valuable information is distilled and an abridged version is produced. Over the last decade, the focus has shifted from single document to multi-document summarization and despite significant progress in the domain, challenges such as sentence ordering and fluency remain. In this paper, a thorough comparison of the several multi-document text summarization techniques such as Machine Learning based, Graph based, Game-Theory based and more has been presented. This paper in its entirety condenses and interprets the numerous approaches, merits and limitations of these techniques. The Benchmark datasets of this domain and their features have also been examined. This survey aims to distinguish the various summarization algorithms based on properties that prove to be valuable in the generation of highly consistent, rational, summaries with reduced redundancy and information richness. The conclusions presented by this paper can be utilized to identify the advantages of these papers which will help future researchers in their study of this domain and ensure the provision of important data for further analysis in a more systematic and comprehensive manner. With the aid of this paper, researchers can identify the areas that present some scope for improvement and thereafter come up with novel or possibly hybrid techniques in Multi-Document Summarization.