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

ATSSI: Abstractive Text Summarization Using Sentiment Infusion

AbstractText Summarization is condensing of text such that, redundant data are removed and important information is extracted and represented in the shortest way possible. With the explosion of the abundant data present on social media, it has become important to analyze this text for seeking information and use it for the advantage of various applications and people. From past few years, this task of automatic summarization has stirred the interest among communities of Natural Language Processing and Text Mining, especially when it comes to opinion summarization. Opinions play a pivotal role in decision making in the society. Other's opinions and suggestions are the base for an individual or a company while making decisions. In this paper, we propose a graph based technique that generates summaries of redundant opinions and uses sentiment analysis to combine the statements. The summaries thus generated are abstraction based summaries and are well formed to convey the gist of the text

Utilizing graph-based representation of text in a hybrid approach to multiple documents summarization

The aim of automatic text summarization is to process text with the purpose of identifying and presenting the most important information appearing in the text. In this research, we aim to investigate automatic multiple document summarization using a hybrid approach of extractive and “shallow abstractive methods. We aim to utilize the graph-based representation approach proposed in [1] and [2] as part of our method to multiple document summarization aiming to provide concise, informative and coherent summaries. We start by scoring sentences based on significance to extract top scoring ones from each document of the set of documents being summarized. In this step, we look into different criteria of scoring sentences, which include: the presence of highly frequent words of the document, the presence of highly frequent words of the set of documents and the presence of words found in the first and last sentence of the document and the different combination of such features. Upon running our experiments we found that the best combination of features to use is utilizing the presence of highly frequent words of the document and presence of words found in the first and last sentences of the document. The average f-score of those features had an average of 7.9% increase to other features\u27 f-scores. Secondly, we address the issue of redundancy of information through clustering sentences of same or similar information into one cluster that will be compressed into one sentence, thus avoiding redundancy of information as much as possible. We investigated clustering the extracted sentences based on two criteria for similarity, the first of which uses word frequency vector for similarity measure and the second of which uses word semantic similarity. Through our experiment, we found that the use of the word vector features yields much better clusters in terms of sentence similarity. The word feature vector had a 20% more number of clusters labeled to contain similar sentences as opposed to those of the word semantic feature. We then adopted a graph-based representation of text proposed in [1] and [2] to represent each sentence in a cluster, and using the k-shortest paths we found the shortest path to represent the final compressed sentence and use it as a final sentence in the summary. Human evaluator scored sentences based on grammatical correctness and almost 74% of 51 sentences evaluated got a perfect score of 2 which is a perfect or near perfect sentence. We finally propose a method for scoring the compressed sentences according to the order in which they should appear in the final summary. We used the Document Understanding Conference dataset for year 2014 as the evaluating dataset for our final system. We used the ROUGE system for evaluation which stands for Recall-Oriented Understudy for Gisting Evaluation. This system compare the automatic summaries to “ideal human references. We also compared our summaries ROUGE scores to those of summaries generated using the MEAD summarization tool. Our system provided better precision and f-score as well as comparable recall scores. On average our system has a percentage increase of 2% for precision and 1.6% increase in f-score than those of MEAD while MEAD has an increase of 0.8% in recall. In addition, our system provided more compressed version of the summary as opposed to that generated by MEAD. We finally ran an experiment to evaluate the order of sentences in the final summary and its comprehensibility where we show that our ordering method produced a comprehensible summary. On average, summaries that scored a perfect score in term of comprehensibility constitute 72% of the evaluated summaries. Evaluators were also asked to count the number of ungrammatical and incomprehensible sentences in the evaluated summaries and on average they were only 10.9% of the summaries sentences. We believe our system provide a \u27shallow abstractive summary to multiple documents that does not require intensive Natural Language Processing.

Multi-document extractive summarization using semantic graph

La generación automática de resúmenes consiste en sintetizar en un texto corto la información más relevante contenida en documentos, y permite reducir los problemas generados por la sobrecarga de información. En este trabajo se presenta un método no supervisado de generación de resúmenes extractivos a partir de múltiples documentos. En esta propuesta, la conceptualización y estructura semántica subyacente del contenido textual se representa en un grafo semántico usando WordNet y se aplica un algoritmo de agrupamiento de conceptos para identificar los tópicos tratados en los documentos, con los cuales se evalúa la relevancia de las oraciones para construir el resumen. El método fue evaluado con corpus de textos de MultiLing 2015, y se usaron métricas de ROUGE para medir la calidad de los resúmenes generados. Los resultados obtenidos se compararon con los de otros sistemas participantes en MultiLing 2015, evidenciándose mejoras en la mayoría de los casos.The automatic texts summarization consists in synthesizing in a short text the most relevant information contained in text documents, and allows to reduce the generated problems by the information overload. In this paper, an unsupervised method for extractive multi-document summarization is presented. In this proposal, the conceptualization and underlying semantics structure of the textual content is represented in a semantic graph using WordNet, and a concept clustering algorithm is applied to identifying the topics of the documents set, with which the relevance of the sentences is evaluated to build the summary. The method was evaluated with texts corpus from MultiLing 2015, and ROUGE metrics were used to measure the quality of the generated summaries. The obtained results were compared with those other participant systems in MultiLing 2015, evidencing improves in most of the cases.Este trabajo ha sido parcialmente soportado por el Fondo Europeo de Desarrollo Regional (FEDER) y el Ministerio Español de Economía y Competitividad, bajo la subvención del proyecto METODOS RIGUROSOS PARA EL INTERNET DEL FUTURO (MERINET) Ref. TIN2016-76843-C4-2-R (AEI/FEDER, UE)

SDbQfSum: Query-focused summarization framework basedon diversity and text semantic analysis

Query-focused multi-document summarization (Qf-MDS) is a sub-task of automatic text summarization that aims to extract a substitute summary from a document cluster of the same topic and based on a user query. Unlike other summarization tasks, Qf-MDS has specific research challenges including the differences and similarities across related document sets, the high degree of redundancy inherent in the summaries created from multiple related sources, relevance to the given query, topic diversity in the produced summary and the small source-to-summary compression ratio. In this work, we propose a semantic diversity feature based query-focused extractive summarizer (SDbQfSum) built on powerful text semantic representation techniques underpinned with Wikipedia commonsense knowledge in order to address the query-relevance, centrality, redundancy and diversity challenges. Specifically, a semantically parsed document text is combined with knowledge-based vectorial representation to extract effective sentence importance and query-relevance features. The proposed monolingual summarizer is evaluated on a standard English dataset for automatic query-focused summarization tasks, that is, the DUC2006 dataset. The obtained results show that our summarizer outperforms most state-of-the-art related approaches on one or more ROUGE measures achieving 0.418, 0.092 and 0.152 in ROUGE-1, ROUGE-2,and ROUGE-SU4 respectively. It also attains competitive performance with the slightly outperforming system(s), for example, the difference between our system's result and best system in ROUGE-1 is just 0.006. We also found through the conducted experiments that our proposed custom cluster merging algorithm significantly reduces information redundancy while maintaining topic diversity across documents