Automatic Summarization System Research Articles

A hybrid approach to managing job offers and candidates

The evolution of the job market has resulted in traditional methods of recruitment becoming insufficient. As it is now necessary to handle volumes of information (mostly in the form of free text) that are impossible to process manually, an analysis and assisted categorization are essential to address this issue. In this paper, we present a combination of the E-Gen and Cortex systems. E-Gen aims to perform analysis and categorization of job offers together with the responses given by the candidates. E-Gen system strategy is based on vectorial and probabilistic models to solve the problem of profiling applications according to a specific job offer. Cortex is a statistical automatic summarization system. In this work, E-Gen uses Cortex as a powerful filter to eliminate irrelevant information contained in candidate answers. Our main objective is to develop a system to assist a recruitment consultant and the results obtained by the proposed combination surpass those of E-Gen in standalone mode on this task.

A text summarizer for Arabic

Automatic text summarization is an essential tool in this era of information overloading. In this paper we present an automatic extractive Arabic text summarization system where the user can cap the size of the final summary. It is a direct system where no machine learning is involved. We use a two pass algorithm where in pass one, we produce a primary summary using Rhetorical Structure Theory (RST); this is followed by the second pass where we assign a score to each of the sentences in the primary summary. These scores will help us in generating the final summary. For the final output, sentences are selected with an objective of maximizing the overall score of the summary whose size should not exceed the user selected limit. We used Rouge to evaluate our system generated summaries of various lengths against those done by a (human) news editorial professional. Experiments on sample texts show our system to outperform some of the existing Arabic summarization systems including those that require machine learning.

Automatic Summarization of Personal Photo Collections

Photo taking and sharing devices (e.g., smart phones, digital cameras, etc) have become extremely popular in recent times. Photo enthusiasts today capture moments of their personal lives using these devices. This has resulted in huge collections of photos stored in various personal archives. The exponential growth of online social networks and web based photo sharing platforms have added fuel to this fire. According to recent estimates [46], three billion photos are uploaded on the social network Facebook per month. This photo data overload has created some major challenges. One of the them is automatic generation of representative overviews from large photo collections. Manual browsing of photo corpora is not only tedious, but also time inefficient. Hence, development of an automatic photo summarization system is not only a research but also a practical challenge. In this dissertation, we present a principled approach for generation of size constrained overview summaries from large personal photo collections.

Double-pass clustering technique for multilingual document collections

It is often necessary to categorize automatically multilingual document sets, in which documents written in a variety of languages are included, into topically homogeneous subsets, such as when applying an automatic summarization system for multilingual news articles. However, there have been few studies on multilingual document clustering to date. In particular, it is not known whether clustering techniques are effective in medium- or large-scale multilingual document sets. For scalability, techniques should be based on dictionary-based translation and a single- or double-pass clustering algorithm. This article reports on experiments of applying multilingual document clustering to medium-scale sets of English, French, German and Italian documents (Reuters news articles). The results show that the double-pass algorithm has a positive effect in the case that each document is translated. On the other hand, the cluster translation strategy in which clusters obtained by applying a clustering algorithm to each language document set are translated has almost no effect. Also, translation disambiguation techniques can improve, but only slightly, the effectiveness of clustering.

Biomedical text summarization to support genetic database curation: using Semantic MEDLINE to create a secondary database of genetic information

This paper examines the development and evaluation of an automatic summarization system in the domain of molecular genetics. The system is a potential component of an advanced biomedical information management application called Semantic MEDLINE and could assist librarians in developing secondary databases of genetic information extracted from the primary literature. An existing summarization system was modified for identifying biomedical text relevant to the genetic etiology of disease. The summarization system was evaluated on the task of identifying data describing genes associated with bladder cancer in MEDLINE citations. A gold standard was produced using records from Genetics Home Reference and Online Mendelian Inheritance in Man. Genes in text found by the system were compared to the gold standard. Recall, precision, and F-measure were calculated. The system achieved recall of 46%, and precision of 88% (F-measure=0.61) by taking Gene References into Function (GeneRIFs) into account. The new summarization schema for genetic etiology has potential as a component in Semantic MEDLINE to support the work of data curators.

Summary of FAQs from a topical forum based on the native composition structure

This research improves the presentation of an automatic multiple-document summarization system, which produces the frequently asked questions (FAQs) of a topical forum. The design and findings of the proposed presentation structure based on a four-part pattern of traditional Chinese articles are presented. An experiment was designed to conduct an objective experimental analysis based on criteria consisting of compression rate, recall rate, and precision rate, as well as a subjective experimental analysis based on user acceptance in terms of the indication, readability, appropriate number of sentences, and structure of the summary. The experimental results show that the proposed summary presentation structure with both domain-terminology corpus methods produced a significantly improved summary presentation compared with the original system. Nevertheless, the FAQ summary presentation system could have used complete sentences instead of partial sentences from the original articles for improved readability.

Fuzzy Swarm Based Text Summarization

Problem statement: The aim of automatic text summarization systems is to select the most relevant information from an abundance of text sources. A daily rapid growth of data on the internet makes the achieve events of such aim a big challenge. Approach: In this study, we incorporated fuzzy logic with swarm intelligence; so that risks, uncertainty, ambiguity and imprecise values of choosing the features weights (scores) could be flexibly tolerated. The weights obtained from the swarm experiment were used to adjust the text features scores and then the features scores were used as inputs for the fuzzy inference system to produce the final sentence score. The sentences were ranked in descending order based on their scores and then the top n sentences were selected as final summary. Results: The experiments showed that the incorporation of fuzzy logic with swarm intelligence could play an important role in the selection process of the most important sentences to be included in the final summary. Also the results showed that the proposed method got a good performance outperforming the swarm model and the benchmark methods. Conclusion: Incorporating more than one technique for dealing with the sentence scoring proved to be an effective mechanism. The PSO was employed for producing the text features weights. The purpose of this process was to emphasize on dealing with the text features fairly based on their importance and to differentiate between more and less important features. The fuzzy inference system was employed to determine the final sentence score, on which the decision was made to include the sentence in the summary or not.

On macro- and micro-level information in multiple documents and its influence on summarization

A well-known challenge for multi-document summarization (MDS) is that a single best or “gold standard” summary does not exist, i.e. it is often difficult to secure a consensus among reference summaries written by different authors. It therefore motivates us to study what the “important information” is in multiple input documents that will guide different authors in writing a summary. In this paper, we propose the notions of macro- and micro-level information. Macro-level information refers to the salient topics shared among different input documents, while micro-level information consists of different sentences that act as elaborating or provide complementary details for those salient topics. Experimental studies were conducted to examine the influence of macro- and micro-level information on summarization and its evaluation. Results showed that human subjects highly relied on macro-level information when writing a summary. The length allowed for summaries is the leading factor that affects the summary agreement. Meanwhile, our summarization evaluation approach based on the proposed macro- and micro-structure information also suggested that micro-level information offered complementary details for macro-level information. We believe that both levels of information form the “important information” which affects the modeling and evaluation of automatic summarization systems.

Automatic summarization of MEDLINE citations for evidence-based medical treatment: A topic-oriented evaluation

As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for 53 diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 ( p < 0.01) and the increase in the overall score of clinical usefulness was 0.39 ( p < 0.05).

Automatic creation and evaluation of MPEG-7 compliant summary descriptions for generic audiovisual content

Today's world is characterized by the increasing availability of audiovisual content, created in many application domains and, thus, with rather different semantic characteristics. Audiovisual content is easily acquired, produced, processed, coded, stored and distributed, progressively making more important the ability for users to selectively consume the large amounts of content available. Automatic audiovisual summarization is a technology playing a major role towards facilitating the user's effective consumption of large amounts of audiovisual data in a reduced amount of time as time is getting more and more precious and scarce. This paper proposes an automatic summarization system for generic audiovisual content based on MPEG-7 compliant summary descriptions. To evaluate the quality of the created summaries, a user evaluation methodology was designed, and applied with promising results, showing that the developed application is able to summarize with success generic audiovisual content, especially high action content. The main novelty of this paper is related to the usage of the MPEG-7 summarization tools in combination with an arousal-based audiovisual summarization model, and to the novel user evaluation methodology and study.

Supervised automatic evaluation for summarization with voted regression model

The high quality evaluation of generated summaries is needed if we are to improve automatic summarization systems. Although human evaluation provides better results than automatic evaluation methods, its cost is huge and it is difficult to reproduce the results. Therefore, we need an automatic method that simulates human evaluation if we are to improve our summarization system efficiently. Although automatic evaluation methods have been proposed, they are unreliable when used for individual summaries. To solve this problem, we propose a supervised automatic evaluation method based on a new regression model called the voted regression model (VRM). VRM has two characteristics: (1) model selection based on ‘corrected AIC’ to avoid multicollinearity, (2) voting by the selected models to alleviate the problem of overfitting. Evaluation results obtained for TSC3 and DUC2004 show that our method achieved error reductions of about 17–51% compared with conventional automatic evaluation methods. Moreover, our method obtained the highest correlation coefficients in several different experiments.

A Supervised Automatic Evaluation for Summarization with a Voted Regression Model

In order to improve automatic summarization systems,

The impact analysis of language differences on an automatic multilingual text summarization system

Based on the salient features of the documents, automatic text summarization systems extract the key sentences from source documents. This process supports the users in evaluating the relevance of ...

The impact analysis of language differences on an automatic multilingual text summarization system

AbstractBased on the salient features of the documents, automatic text summarization systems extract the key sentences from source documents. This process supports the users in evaluating the relevance of the extracted documents returned by information retrieval systems. Because of this tool, efficient filtering can be achieved. Indirectly, these systems help to resolve the problem of information overloading. Many automatic text summarization systems have been implemented for use with different languages. It has been established that the grammatical and lexical differences between languages have a significant effect on text processing. However, the impact of the language differences on the automatic text summarization systems has not yet been investigated. The authors provide an impact analysis of language difference on automatic text summarization. It includes the effect on the extraction processes, the scoring mechanisms, the performance, and the matching of the extracted sentences, using the parallel corpus in English and Chinese as the tested object. The analysis results provide a greater understanding of language differences and promote the future development of more advanced text summarization techniques.

Automatic video summarization based on MPEG-7 descriptions

The ever-growing amount of audiovisual content available has raised the need to develop systems allowing each of us to consume the information considered ‘essential’, adapted to our tastes, our preferences, our time and also to our capacities of receiving, consuming and storing that information. In other words, there is an increasing need to develop systems able to automatically summarize audiovisual information. This paper proposes a novel query-based summary creation mechanism using a relevance metric and a constraints schema implemented in the context of an automatic video summarization system based on MPEG-7 descriptions. In the context of the same system, this paper also proposes a human skin filter allowing to build summaries based on the presence or absence of human skin. This skin colour filter is based solely on the MPEG-7 Dominant Colour descriptor, which means that the content is skin filtered with a rather small amount of processing, without accessing and processing the video data.