Extractive Multi-document Summarization Research Articles

Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization

The central challenge in Automatic Text Summarization (ATS) involves efficiently generating machine-generated text summaries through optimization algorithms. An ATS is a critical component for systems dealing with textual information processing. However, the current approach faces a significant hurdle due to the computational intensity of the process, particularly when employing complex optimization techniques like swarm intelligence optimization alongside a costly ATS repair operator. While the current approach yields impressive Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics for the generated summary, it struggles with inefficiencies, mainly attributed to the substantial optimization time consumed by the ATS repair operator scheme. In order to address this, a novel solution called Decomposition-based Multi-Objective Differential Evolution (MODE/D) is proposed. It is built upon the foundation of Differential Evolution for Multi-Objective Optimization (DEMO) and the weighted sum method (WS), coupled with an innovative ATS repair operator scheme. Through experimentation on Document Understanding Conferences (DUC) datasets, the novel approach of MODE/D – WS is validated by evaluating the results using ROUGE metrics. The outcomes are twofold: a remarkable reduction in serial execution time and a noteworthy enhancement over existing techniques in the scholarly domain, as evidenced by improved ROUGE-1, ROUGE-2, and ROUGE-L scores.

Occams: A Text Summarization Package

Extractive text summarization selects asmall subset of sentences from a document, which gives good “coverage” of a document. When given a set of term weights indicating the importance of the terms, the concept of coverage may be formalized into a combinatorial optimization problem known as the budgeted maximum coverage problem. Extractive methods in this class are known to beamong the best of classic extractive summarization systems. This paper gives a synopsis of thesoftware package occams, which is a multilingual extractive single and multi-document summarization package based on an algorithm giving an optimal approximation to the budgeted maximum coverage problem. The occams package is written in Python and provides an easy-to-use modular interface, allowing it to work in conjunction with popular Python NLP packages, such as nltk, stanza or spacy.

SGCSumm: An extractive multi-document summarization method based on pre-trained language model, submodularity, and graph convolutional neural networks

The increase in online text generation by humans and machines needs automatic text summarization systems. Recent research studies commonly use deep learning, besides sentence embedding and feature learning mechanisms, to find a solution for text summarization. But they ignore the fact that while finding the optimal solution for extractive text summarization is NP-hard, how do they ensure the quality of their solution? In our previous work, an extractive summarizer, called DSNSum, was proposed based on deep submodular network (DSN) that uses handcrafted features. It leverages submodularity to guarantee a minimum bound for performance. In this paper, submodular graph convolutional summarizer (SGCSumm), an extractive multi-document summarization method, is presented where DSN is used to guarantee a minimum performance bound. It addresses DSNSum’s shortcomings and makes enhancements that yield a summarizer supporting sentence embedding and graph structure-aware feature learning. It takes a formal approach to show how the enhancements are possible without losing the guarantee of a minimum bound for performance. Finally, to evaluate the performance of the proposed summarization method, the DUC 2004 and DailyMail/CNN datasets are used. The experimental results show that the performance of SGCSumm is comparable to that of state-of-the-art summarization methods in terms of ROUGE scores.

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.

Query-Oriented Multi-Document Summarization via Unsupervised Deep Learning

Extractive style query-oriented multi-document summarization generates the summary by extracting a proper set of sentences from multiple documents based on the pre-given query. This paper proposes a novel multi-document summarization framework via deep learning model. This uniform framework consists of three parts: concepts extraction, summary generation, and reconstruction validation, which work together to achieve the largest coverage of the documents content. A new query-oriented extraction technique is proposed to concentrate distributed information to hidden units layer by layer. Then, the whole deep architecture is fine-tuned by minimizing the information loss of reconstruction validation. According to the concentrated information, dynamic programming is used to seek most informative set of sentences as the summary. Experiments on three benchmark datasets demonstrate the effectiveness of the proposed framework and algorithms.

Unsupervised query-focused multi-document summarization based on transfer learning from sentence embedding models, BM25 model, and maximal marginal relevance criterion

Extractive query-focused multi-document summarization (QF-MDS) is the process of automatically generating an informative summary from a collection of documents that answers a pre-given query. Sentence and query representation is a fundamental cornerstone that affects the effectiveness of several QF-MDS methods. Transfer learning using pre-trained word embedding models has shown promising performance in many applications. However, most of these representations do not consider the order and the semantic relationships between words in a sentence, and thus they do not carry the meaning of a full sentence. In this paper, to deal with this issue, we propose to leverage transfer learning from pre-trained sentence embedding models to represent documents’ sentences and users’ queries using embedding vectors that capture the semantic and the syntactic relationships between their constituents (words, phrases). Furthermore, BM25 and semantic similarity function are linearly combined to retrieve a subset of sentences based on their relevance to the query. Finally, the maximal marginal relevance criterion is applied to re-rank the selected sentences by maintaining query relevance and minimizing redundancy. The proposed method is unsupervised, simple, efficient, and requires no labeled text summarization training data. Experiments are conducted using three standard datasets from the DUC evaluation campaign (DUC’2005–2007). The overall obtained results show that our method outperforms several state-of-the-art systems and achieves comparable results to the best performing systems, including supervised deep learning-based methods.

Unsupervised extractive multi-document summarization method based on transfer learning from BERT multi-task fine-tuning

Text representation is a fundamental cornerstone that impacts the effectiveness of several text summarization methods. Transfer learning using pre-trained word embedding models has shown promising results. However, most of these representations do not consider the order and the semantic relationships between words in a sentence, and thus they do not carry the meaning of a full sentence. To overcome this issue, the current study proposes an unsupervised method for extractive multi-document summarization based on transfer learning from BERT sentence embedding model. Moreover, to improve sentence representation learning, we fine-tune BERT model on supervised intermediate tasks from GLUE benchmark datasets using single-task and multi-task fine-tuning methods. Experiments are performed on the standard DUC’2002–2004 datasets. The obtained results show that our method has significantly outperformed several baseline methods and achieves a comparable and sometimes better performance than the recent state-of-the-art deep learning–based methods. Furthermore, the results show that fine-tuning BERT using multi-task learning has considerably improved the performance.

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.

Extractive Multi-Document Summarization: A Review of Progress in the Last Decade

With the tremendous growth in the number of electronic documents, it is becoming challenging to manage the volume of information. Much research has focused on automatically summarizing the information available in the documents. Multi-Document Summarization (MDS) is one approach that aims to extract the information from the available documents in such a concise way that none of the important points are missed from the summary while avoiding the redundancy of information at the same time. This study presents an extensive survey of extractive MDS over the last decade to show the progress of research in this field. We present different techniques of extractive MDS and compare their strengths and weaknesses. Research work is presented by category and evaluated to help the reader understand the work in this field and to guide them in defining their own research directions. Benchmark datasets and standard evaluation techniques are also presented. This study concludes that most of the extractive MDS techniques are successful in developing salient and information-rich summaries of the documents provided.

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.

Text Summarization as a Multi-objective Optimization Task: Applying Harmony Search to Extractive Multi-Document Summarization

Abstract Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a representative summary. Therefore, probing for the best sentences is a fundamental task. This paper has coped with extractive summarization as a multi-objective optimization problem and proposed a language-independent, semantic-aware approach that applies the harmony search algorithm to generate appropriate multi-document summaries. It learns the objective function from an extra set of reference summaries and then generates the best summaries according to the trained function. The system also performs some supplementary activities for better achievements. It expands the sentences by using an inventive approach that aims at tuning conceptual densities in the sentences towards important topics. Furthermore, we introduced an innovative clustering method for identifying important topics and reducing redundancies. A sentence placement policy based on the Hamiltonian shortest path was introduced for producing readable summaries. The experiments were conducted on DUC2002, DUC2006 and DUC2007 datasets. Experimental results showed that the proposed framework could assist the summarization process and yield better performance. Also, it was able to generally outperform other cited summarizer systems.

Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization

For many NLP applications, such as question answering and summarization, the goal is to select the best solution from a large space of candidates to meet a particular user’s needs. To address the lack of user or task-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method uses Bayesian optimization to focus the user’s labeling effort on high quality candidates and integrate prior knowledge to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive multidocument summarization, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarization.

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.

An unsupervised method for extractive multi-document summarization based on centroid approach and sentence embeddings

Extractive multi-document summarization (MDS) is the process of automatically summarizing a collection of documents by ranking sentences according to their importance and informativeness. Text representation is a fundamental process that affects the effectiveness of many text summarization methods. Word embedding representations have shown to be effective for several Natural Language Processing (NLP) tasks including Automatic Text Summarization (ATS). However, most of these representations do not consider the order and the semantic relationships between words in a sentence. This does not fully allow grasping the sentence semantics and the syntactic relationships between sentences constituents. In this paper, to overcome this problem, we propose an unsupervised method for generic extractive multi-document summarization based on the sentence embedding representations and the centroid approach. The proposed method selects relevant sentences according to the final score obtained by combining three scores: sentence content relevance, sentence novelty, and sentence position scores. The sentence content relevance score is computed as the cosine similarity between the centroid embedding vector of the cluster of documents and the sentence embedding vectors. The sentence novelty metric is explicitly adopted to deal with redundancy. The sentence position metric assumes that the first sentences of a document are more relevant to the summary, and it assigns high scores to these sentences. Moreover, this paper provides a comparative analysis of nine sentence embedding models used to represent sentences as dense vectors in a low dimensional vector space in the context of extractive multi-document summarization. Experiments are performed on the standard DUC’2002–2004 benchmark datasets and the Multi-News dataset. The overall obtained results have shown that our method outperforms several state-of-the-art methods and achieves promising results compared to the best performing methods including supervised deep learning based methods.

Un estudio de los métodos de reducción del frente de Pareto a una única solución aplicado al problema de resumen extractivo multi-documento

Automatic summarization methods are currently needed in many different contexts. The extractive multi-document summarization problem tries to cover the main content of a document collection and to reduce the redundant information. The best way to address this task is through a multi-objective optimization approach. The result of this approach is a set of non-dominated solutions or Pareto set. However, since only one summary is needed, the Pareto front must be reduced to a single solution. For this, several methods have been considered, such as the largest hypervolume, the consensus solution, the shortest distance to the ideal point, and the shortest distance to all points. The methods have been tested using datasets from DUC, and they have been evaluated with ROUGE metrics. The results show that consensus solution achieves the best average values.

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.