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1.
Summarizing court decisions 总被引：2，自引：0，他引：2
In the field of law there is an absolute need for summarizing the texts of court decisions in order to make the content of the cases easily accessible for legal professionals. During the SALOMON and MOSAIC2 projects we investigated the summarization and retrieval of legal cases. This article presents some of the main findings while integrating the research results of experiments on legal document summarization by other research groups. In addition, we propose novel avenues of research for automatic text summarization, which we currently exploit when summarizing court decisions in the ACILA3 project. Techniques for automated concept learning and argument recognition are here the most challenging. 相似文献
2.
《Information processing & management》2022,59(3):102913
Abstractive summarization aims to generate a concise summary covering salient content from single or multiple text documents. Many recent abstractive summarization methods are built on the transformer model to capture long-range dependencies in the input text and achieve parallelization. In the transformer encoder, calculating attention weights is a crucial step for encoding input documents. Input documents usually contain some key phrases conveying salient information, and it is important to encode these phrases completely. However, existing transformer-based summarization works did not consider key phrases in input when determining attention weights. Consequently, some of the tokens within key phrases only receive small attention weights, which is not conducive to encoding the semantic information of input documents. In this paper, we introduce some prior knowledge of key phrases into the transformer-based summarization model and guide the model to encode key phrases. For the contextual representation of each token in the key phrase, we assume the tokens within the same key phrase make larger contributions compared with other tokens in the input sequence. Based on this assumption, we propose the Key Phrase Aware Transformer (KPAT), a model with the highlighting mechanism in the encoder to assign greater attention weights for tokens within key phrases. Specifically, we first extract key phrases from the input document and score the phrases’ importance. Then we build the block diagonal highlighting matrix to indicate these phrases’ importance scores and positions. To combine self-attention weights with key phrases’ importance scores, we design two structures of highlighting attention for each head and the multi-head highlighting attention. Experimental results on two datasets (Multi-News and PubMed) from different summarization tasks and domains show that our KPAT model significantly outperforms advanced summarization baselines. We conduct more experiments to analyze the impact of each part of our model on the summarization performance and verify the effectiveness of our proposed highlighting mechanism. 相似文献
3.
Weighted consensus multi-document summarization 总被引：1，自引：0，他引：1
Multi-document summarization is a fundamental tool for document understanding and has received much attention recently. Given a collection of documents, a variety of summarization methods based on different strategies have been proposed to extract the most important sentences from the original documents. However, very few studies have been reported on aggregating different summarization methods to possibly generate better summary results. In this paper, we propose a weighted consensus summarization method to combine the results from single summarization systems. We evaluate and compare our proposed weighted consensus method with various baseline combination methods. Experimental results on DUC2002 and DUC2004 data sets demonstrate the performance improvement by aggregating multiple summarization systems, and our proposed weighted consensus summarization method outperforms other combination methods. 相似文献
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5.
Generating gene summaries from biomedical literature: A study of semi-structured summarization 总被引：3，自引：0，他引：3
Xu Ling Jing Jiang Xin He Qiaozhu Mei Chengxiang Zhai Bruce Schatz 《Information processing & management》2007,43(6):1777
Most knowledge accumulated through scientific discoveries in genomics and related biomedical disciplines is buried in the vast amount of biomedical literature. Since understanding gene regulations is fundamental to biomedical research, summarizing all the existing knowledge about a gene based on literature is highly desirable to help biologists digest the literature. In this paper, we present a study of methods for automatically generating gene summaries from biomedical literature. Unlike most existing work on automatic text summarization, in which the generated summary is often a list of extracted sentences, we propose to generate a semi-structured summary which consists of sentences covering specific semantic aspects of a gene. Such a semi-structured summary is more appropriate for describing genes and poses special challenges for automatic text summarization. We propose a two-stage approach to generate such a summary for a given gene – first retrieving articles about a gene and then extracting sentences for each specified semantic aspect. We address the issue of gene name variation in the first stage and propose several different methods for sentence extraction in the second stage. We evaluate the proposed methods using a test set with 20 genes. Experiment results show that the proposed methods can generate useful semi-structured gene summaries automatically from biomedical literature, and our proposed methods outperform general purpose summarization methods. Among all the proposed methods for sentence extraction, a probabilistic language modeling approach that models gene context performs the best. 相似文献
6.
为系统了解国内机构知识库研究现状与趋势,采用文献计量法、比较分析法等,以CSSCI收录的机构知识库来源文献和被引文献数据为基础,从来源文献、引文情况及被引情况3个角度,对载文情况、引文概况、引文语种、引文类型、作者情况、期刊情况、被引情况、被引成果等多个方面进行统计、分析,并通过对这些文献的阅读、关键词分析和国内外有关研究情况的比较,针对国内机构知识库研究的问题,提出了4项建议。 相似文献
7.
Previous studies have repeatedly demonstrated that the relevance of a citing document is related to the number of times with which the source document is cited. Despite the ease with which electronic documents would permit the incorporation of this information into citation-based document search and retrieval systems, the possibilities of repeated citations remain untapped. Part of this under-utilization may be due to the fact that very little is known regarding the pattern of repeated citations in scholarly literature or how this pattern may vary as a function of journal, academic discipline or self-citation. The current research addresses these unanswered questions in order to facilitate the future incorporation of repeated citation information into document search and retrieval systems. Using data mining of electronic texts, the citation characteristics of nine different journals, covering the three different academic fields (economics, computing, and medicine & biology), were characterized. It was found that the frequency (f) with which a reference is cited N or more times within a document is consistent across the sampled journals and academic fields. Self-citation causes an increase in frequency, and this effect becomes more pronounced for large N. The objectivity, automatability, and insensitivity of repeated citations to journal and discipline, present powerful opportunities for improving citation-based document search. 相似文献
8.
《Information processing & management》2020,57(6):102341
Today, due to a vast amount of textual data, automated extractive text summarization is one of the most common and practical techniques for organizing information. Extractive summarization selects the most appropriate sentences from the text and provide a representative summary. The sentences, as individual textual units, usually are too short for major text processing techniques to provide appropriate performance. Hence, it seems vital to bridge the gap between short text units and conventional text processing methods.In this study, we propose a semantic method for implementing an extractive multi-document summarizer system by using a combination of statistical, machine learning based, and graph-based methods. It is a language-independent and unsupervised system. The proposed framework learns the semantic representation of words from a set of given documents via word2vec method. It expands each sentence through an innovative method with the most informative and the least redundant words related to the main topic of sentence. Sentence expansion implicitly performs word sense disambiguation and tunes the conceptual densities towards the central topic of each sentence. Then, it estimates the importance of sentences by using the graph representation of the documents. To identify the most important topics of the documents, we propose an inventive clustering approach. It autonomously determines the number of clusters and their initial centroids, and clusters sentences accordingly. The system selects the best sentences from appropriate clusters for the final summary with respect to information salience, minimum redundancy, and adequate coverage.A set of extensive experiments on DUC2002 and DUC2006 datasets was conducted for investigating the proposed scheme. Experimental results showed that the proposed sentence expansion algorithm and clustering approach could considerably enhance the performance of the summarization system. Also, comparative experiments demonstrated that the proposed framework outperforms most of the state-of-the-art summarizer systems and can impressively assist the task of extractive text summarization. 相似文献
9.
《Research Policy》2022,51(4):104484
Although citations are widely used to measure the influence of scientific works, research shows that many citations serve rhetorical functions and reflect little-to-no influence on the citing authors. If highly cited papers disproportionately attract rhetorical citations then their citation counts may reflect rhetorical usefulness more than influence. Alternatively, researchers may perceive highly cited papers to be of higher quality and invest more effort into reading them, leading to disproportionately substantive citations. We test these arguments using data on 17,154 randomly sampled citations collected via surveys from 9,380 corresponding authors in 15 fields. We find that most citations (54%) had little-to-no influence on the citing authors. However, citations to the most highly cited papers were 2–3 times more likely to denote substantial influence. Experimental and correlational data show a key mechanism: displaying low citation counts lowers perceptions of a paper's quality, and papers with poor perceived quality are read more superficially. The results suggest that higher citation counts lead to more meaningful engagement from readers and, consequently, the most highly cited papers influence the research frontier much more than their raw citation counts imply. 相似文献
10.
《Information processing & management》2022,59(6):103069
Estimating the similarity between two legal case documents is an important and challenging problem, having various downstream applications such as prior-case retrieval and citation recommendation. There are two broad approaches for the task — citation network-based and text-based. Prior citation network-based approaches consider citations only to prior-cases (also called precedents) (PCNet). This approach misses important signals inherent in Statutes (written laws of a jurisdiction). In this work, we propose Hier-SPCNet that augments PCNet with a heterogeneous network of Statutes. We incorporate domain knowledge for legal document similarity into Hier-SPCNet, thereby obtaining state-of-the-art results for network-based legal document similarity.Both textual and network similarity provide important signals for legal case similarity; but till now, only trivial attempts have been made to unify the two signals. In this work, we apply several methods for combining textual and network information for estimating legal case similarity. We perform extensive experiments over legal case documents from the Indian judiciary, where the gold standard similarity between document-pairs is judged by law experts from two reputed Law institutes in India. Our experiments establish that our proposed network-based methods significantly improve the correlation with domain experts’ opinion when compared to the existing methods for network-based legal document similarity. Our best-performing combination method (that combines network-based and text-based similarity) improves the correlation with domain experts’ opinion by 11.8% over the best text-based method and 20.6% over the best network-based method. We also establish that our best-performing method can be used to recommend/retrieve citable and similar cases for a source (query) case, which are well appreciated by legal experts. 相似文献
11.
《Information processing & management》2020,57(3):102187
Increases in the amount of text resources available via the Internet has amplified the need for automated document summarizing tools. However, further efforts are needed in order to improve the quality of the existing summarization tools currently available. The current study proposes Karcı Summarization, a novel methodology for extractive, generic summarization of text documents. Karcı Entropy was used for the first time in a document summarization method within a unique approach. An important feature of the proposed system is that it does not require any kind of information source or training data. At the stage of presenting the input text, a tool for text processing was introduced; known as KUSH (named after its authors; Karcı, Uçkan, Seyyarer, and Hark), and is used to protect semantic consistency between sentences. The Karcı Entropy-based solution chooses the most effective, generic and most informational sentences within a paragraph or unit of text. Experimentation with the Karcı Summarization approach was tested using open-access document text (Document Understanding Conference; DUC-2002, DUC-2004) datasets. Performance achievement of the Karcı Summarization approach was calculated using metrics known as Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The experimental results showed that the proposed summarizer outperformed all current state-of-the-art methods in terms of 200-word summaries in the metrics of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-W-1.2. In addition, the proposed summarizer outperformed the nearest competitive summarizers by a factor of 6.4% for ROUGE-1 Recall on the DUC-2002 dataset. These results demonstrate that Karcı Summarization is a promising technique and it is therefore expected to attract interest from researchers in the field. Our approach was shown to have a high potential for adoptability. Moreover, the method was assessed as quite insensitive to disorderly and missing texts due to its KUSH text processing module. 相似文献
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13.
Minh-Tien Nguyen Viet Cuong Tran Xuan Hoai Nguyen Le-Minh Nguyen 《Information processing & management》2019,56(3):495-515
In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods. 相似文献
14.
网络文献作为引文的理性思考 总被引：1，自引：0，他引：1
针对当前学术界利用网络文献作为学术论文引文的现状，分析了网络文献作为引文存在的几个问题，在此基础上提出网络文献引用及其规范化建议。 相似文献
15.
High quality summary is the target and challenge for any automatic text summarization. In this paper, we introduce a different hybrid model for automatic text summarization problem. We exploit strengths of different techniques in building our model: we use diversity-based method to filter similar sentences and select the most diverse ones, differentiate between the more important and less important features using the swarm-based method and use fuzzy logic to make the risks, uncertainty, ambiguity and imprecise values of the text features weights flexibly tolerated. The diversity-based method focuses to reduce redundancy problems and the other two techniques concentrate on the scoring mechanism of the sentences. We presented the proposed model in two forms. In the first form of the model, diversity measures dominate the behavior of the model. In the second form, the diversity constraint is no longer imposed on the model behavior. That means the diversity-based method works same as fuzzy swarm-based method. The results showed that the proposed model in the second form performs better than the first form, the swarm model, the fuzzy swarm method and the benchmark methods. Over results show that combination of diversity measures, swarm techniques and fuzzy logic can generate good summary containing the most important parts in the document. 相似文献
16.
《Information processing & management》2020,57(6):102264
Searching the Internet for a certain topic can become a daunting task because users cannot read and comprehend all the resulting texts. Automatic Text summarization (ATS) in this case is clearly beneficial because manual summarization is expensive and time-consuming. To enhance ATS for single documents, this paper proposes a novel extractive graph-based framework “EdgeSumm” that relies on four proposed algorithms. The first algorithm constructs a new text graph model representation from the input document. The second and third algorithms search the constructed text graph for sentences to be included in the candidate summary. When the resulting candidate summary still exceeds a user-required limit, the fourth algorithm is used to select the most important sentences. EdgeSumm combines a set of extractive ATS methods (namely graph-based, statistical-based, semantic-based, and centrality-based methods) to benefit from their advantages and overcome their individual drawbacks. EdgeSumm is general for any document genre (not limited to a specific domain) and unsupervised so it does not require any training data. The standard datasets DUC2001 and DUC2002 are used to evaluate EdgeSumm using the widely used automatic evaluation tool: Recall-Oriented Understudy for Gisting Evaluation (ROUGE). EdgeSumm gets the highest ROUGE scores on DUC2001. For DUC2002, the evaluation results show that the proposed framework outperforms the state-of-the-art ATS systems by achieving improvements of 1.2% and 4.7% over the highest scores in the literature for the metrics of ROUGE-1 and ROUGE-L respectively. In addition, EdgeSumm achieves very competitive results for the metrics of ROUGE-2 and ROUGE-SU4. 相似文献
17.
John O&#x;Connor 《Information processing & management》1982,18(3):125-131
Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite.To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73% of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents.The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50%. Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70%. Only words from citing statements published within a year of the cited papers were used.The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40%. When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50%. 相似文献
18.
《Information processing & management》2022,59(2):102798
Automated legal text classification is a prominent research topic in the legal field. It lays the foundation for building an intelligent legal system. Current literature focuses on international legal texts, such as Chinese cases, European cases, and Australian cases. Little attention is paid to text classification for U.S. legal texts. Deep learning has been applied to improving text classification performance. Its effectiveness needs further exploration in domains such as the legal field. This paper investigates legal text classification with a large collection of labeled U.S. case documents through comparing the effectiveness of different text classification techniques. We propose a machine learning algorithm using domain concepts as features and random forests as the classifier. Our experiment results on 30,000 full U.S. case documents in 50 categories demonstrated that our approach significantly outperforms a deep learning system built on multiple pre-trained word embeddings and deep neural networks. In addition, applying only the top 400 domain concepts as features for building the random forests could achieve the best performance. This study provides a reference to select machine learning techniques for building high-performance text classification systems in the legal domain or other fields. 相似文献
19.
通过对农业网页的HTML结构和特征研究,叙述基于文本内容的农业网页信息抽取和分类实验研究过程。实验中利用DOM结构对农业网页信息进行信息抽取和预处理,并根据文本的内容自动计算文本类别属性,得到特征词,通过总结样本文档的特征,对遇到的新文档进行自动分类。实验结果表明,本文信息提取的时间复杂度比较小、精确度高,提高了分类的正确率。 相似文献
20.
Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel integrated information retrieval system—the Query, Cluster, Summarize (QCS) system—which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of methods in the QCS design improves retrievals by providing users more focused information organized by topic.We demonstrate the improved performance by a series of experiments using standard test sets from the Document Understanding Conferences (DUC) as measured by the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines.Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence “trimming” and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format.Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules. 相似文献