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1.
DUC in context 总被引：1，自引：0，他引：1
Recent years have seen increased interest in text summarization with emphasis on evaluation of prototype systems. Many factors can affect the design of such evaluations, requiring choices among competing alternatives. This paper examines several major themes running through three evaluations: SUMMAC, NTCIR, and DUC, with a concentration on DUC. The themes are extrinsic and intrinsic evaluation, evaluation procedures and methods, generic versus focused summaries, single- and multi-document summaries, length and compression issues, extracts versus abstracts, and issues with genre. 相似文献
2.
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion 总被引：1，自引：0，他引：1
Lucy Vanderwende Hisami Suzuki Chris Brockett Ani Nenkova 《Information processing & management》2007,43(6):1606
In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems. 相似文献
3.
Automatic text summarization attempts to provide an effective solution to today’s unprecedented growth of textual data. This paper proposes an innovative graph-based text summarization framework for generic single and multi document summarization. The summarizer benefits from two well-established text semantic representation techniques; Semantic Role Labelling (SRL) and Explicit Semantic Analysis (ESA) as well as the constantly evolving collective human knowledge in Wikipedia. The SRL is used to achieve sentence semantic parsing whose word tokens are represented as a vector of weighted Wikipedia concepts using ESA method. The essence of the developed framework is to construct a unique concept graph representation underpinned by semantic role-based multi-node (under sentence level) vertices for summarization. We have empirically evaluated the summarization system using the standard publicly available dataset from Document Understanding Conference 2002 (DUC 2002). Experimental results indicate that the proposed summarizer outperforms all state-of-the-art related comparators in the single document summarization based on the ROUGE-1 and ROUGE-2 measures, while also ranking second in the ROUGE-1 and ROUGE-SU4 scores for the multi-document summarization. On the other hand, the testing also demonstrates the scalability of the system, i.e., varying the evaluation data size is shown to have little impact on the summarizer performance, particularly for the single document summarization task. In a nutshell, the findings demonstrate the power of the role-based and vectorial semantic representation when combined with the crowd-sourced knowledge base in Wikipedia. 相似文献
4.
Danushka Bollegala Naoaki Okazaki Mitsuru Ishizuka 《Information processing & management》2010,46(1):89-109
Ordering information is a difficult but important task for applications generating natural language texts such as multi-document summarization, question answering, and concept-to-text generation. In multi-document summarization, information is selected from a set of source documents. However, improper ordering of information in a summary can confuse the reader and deteriorate the readability of the summary. Therefore, it is vital to properly order the information in multi-document summarization. We present a bottom-up approach to arrange sentences extracted for multi-document summarization. To capture the association and order of two textual segments (e.g. sentences), we define four criteria: chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion, until we obtain the overall segment with all sentences arranged. We evaluate the sentence orderings produced by the proposed method and numerous baselines using subjective gradings as well as automatic evaluation measures. We introduce the average continuity, an automatic evaluation measure of sentence ordering in a summary, and investigate its appropriateness for this task. 相似文献
5.
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. 相似文献
6.
Automated summaries help tackle the ever growing volume of information floating around. There are two broad categories: extract and abstract. In the former we retain the more important sentences more or less in their original structure, while the latter requires a fusion of multiple sentences and/or paraphrasing. This is a more challenging task than extract summaries. In this paper, we present a novel generic abstract summarizer for a single document in Arabic language. The system starts by segmenting the input text topic wise. Then, each textual segment is extractively summarized. Finally, we apply rule-based sentence reduction technique. The RST-based extractive summarizer is an enhanced version of the system in Azmi and Al-Thanyyan (2012). By controlling the size of the extract summary of each segment we can cap the size of the final abstractive summary. Both summarizers, the enhanced extractive and the abstractive, were evaluated. We tested our enhanced extractive summarizer on the same dataset in the aforementioned paper, using the measures recall, precision and Rouge. The results show noticeable improvement in the performance, specially the precision in shorter summaries. The abstractive summarizer was tested on a set of 150 documents, generating summaries of sizes 50%, 40%, 30% and 20% (of the original’s word count). The results were assessed by two human experts who graded them out of a maximum score of 5. The average score ranged between 4.53 and 1.92 for summaries at different granularities, with shorter summaries receiving the lower score. The experimental results are encouraging and demonstrate the effectiveness of our approach. 相似文献
7.
The purpose of extractive speech summarization is to automatically select a number of indicative sentences or paragraphs (or audio segments) from the original spoken document according to a target summarization ratio and then concatenate them to form a concise summary. Much work on extractive summarization has been initiated for developing machine-learning approaches that usually cast important sentence selection as a two-class classification problem and have been applied with some success to a number of speech summarization tasks. However, the imbalanced-data problem sometimes results in a trained speech summarizer with unsatisfactory performance. Furthermore, training the summarizer by improving the associated classification accuracy does not always lead to better summarization evaluation performance. In view of such phenomena, we present in this paper an empirical investigation of the merits of two schools of training criteria to alleviate the negative effects caused by the aforementioned problems, as well as to boost the summarization performance. One is to learn the classification capability of a summarizer on the basis of the pair-wise ordering information of sentences in a training document according to a degree of importance. The other is to train the summarizer by directly maximizing the associated evaluation score or optimizing an objective that is linked to the ultimate evaluation. Experimental results on the broadcast news summarization task suggest that these training criteria can give substantial improvements over a few existing summarization methods. 相似文献
8.
Most existing research on applying machine learning techniques to document summarization explores either classification models or learning-to-rank models. This paper presents our recent study on how to apply a different kind of learning models, namely regression models, to query-focused multi-document summarization. We choose to use Support Vector Regression (SVR) to estimate the importance of a sentence in a document set to be summarized through a set of pre-defined features. In order to learn the regression models, we propose several methods to construct the “pseudo” training data by assigning each sentence with a “nearly true” importance score calculated with the human summaries that have been provided for the corresponding document set. A series of evaluations on the DUC data sets are conducted to examine the efficiency and the robustness of the proposed approaches. When compared with classification models and ranking models, regression models are consistently preferable. 相似文献
9.
Jonas Sjbergh 《Information processing & management》2007,43(6):1500
We show some limitations of the ROUGE evaluation method for automatic summarization. We present a method for automatic summarization based on a Markov model of the source text. By a simple greedy word selection strategy, summaries with high ROUGE-scores are generated. These summaries would however not be considered good by human readers. The method can be adapted to trick different settings of the ROUGEeval package. 相似文献
10.
Tsutomu Hirao Manabu Okumura Norihito Yasuda Hideki Isozaki 《Information processing & management》2007,43(6):1521
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. 相似文献
11.
Liana Ermakova Jean Valère Cossu Josiane Mothe 《Information processing & management》2019,56(5):1794-1814
The increasing volume of textual information on any topic requires its compression to allow humans to digest it. This implies detecting the most important information and condensing it. These challenges have led to new developments in the area of Natural Language Processing (NLP) and Information Retrieval (IR) such as narrative summarization and evaluation methodologies for narrative extraction. Despite some progress over recent years with several solutions for information extraction and text summarization, the problems of generating consistent narrative summaries and evaluating them are still unresolved. With regard to evaluation, manual assessment is expensive, subjective and not applicable in real time or to large collections. Moreover, it does not provide re-usable benchmarks. Nevertheless, commonly used metrics for summary evaluation still imply substantial human effort since they require a comparison of candidate summaries with a set of reference summaries. The contributions of this paper are three-fold. First, we provide a comprehensive overview of existing metrics for summary evaluation. We discuss several limitations of existing frameworks for summary evaluation. Second, we introduce an automatic framework for the evaluation of metrics that does not require any human annotation. Finally, we evaluate the existing assessment metrics on a Wikipedia data set and a collection of scientific articles using this framework. Our findings show that the majority of existing metrics based on vocabulary overlap are not suitable for assessment based on comparison with a full text and we discuss this outcome. 相似文献
12.
This paper presents a novel query expansion method, which is combined in the graph-based algorithm for query-focused multi-document summarization, so as to resolve the problem of information limit in the original query. Our approach makes use of both the sentence-to-sentence relations and the sentence-to-word relations to select the query biased informative words from the document set and use them as query expansions to improve the sentence ranking result. Compared to previous query expansion approaches, our approach can capture more relevant information with less noise. We performed experiments on the data of document understanding conference (DUC) 2005 and DUC 2006, and the evaluation results show that the proposed query expansion method can significantly improve the system performance and make our system comparable to the state-of-the-art systems. 相似文献
13.
The rise in the amount of textual resources available on the Internet has created the need for tools of automatic document summarization. The main challenges of query-oriented extractive summarization are (1) to identify the topics of the documents and (2) to recover query-relevant sentences of the documents that together cover these topics. Existing graph- or hypergraph-based summarizers use graph-based ranking algorithms to produce individual scores of relevance for the sentences. Hence, these systems fail to measure the topics jointly covered by the sentences forming the summary, which tends to produce redundant summaries. To address the issue of selecting non-redundant sentences jointly covering the main query-relevant topics of a corpus, we propose a new method using the powerful theory of hypergraph transversals. First, we introduce a new topic model based on the semantic clustering of terms in order to discover the topics present in a corpus. Second, these topics are modeled as the hyperedges of a hypergraph in which the nodes are the sentences. A summary is then produced by generating a transversal of nodes in the hypergraph. Algorithms based on the theory of submodular functions are proposed to generate the transversals and to build the summaries. The proposed summarizer outperforms existing graph- or hypergraph-based summarizers by at least 6% of ROUGE-SU4 F-measure on DUC 2007 dataset. It is moreover cheaper than existing hypergraph-based summarizers in terms of computational time complexity. 相似文献
14.
We present two approaches to email thread summarization: collective message summarization (CMS) applies a multi-document summarization approach, while individual message summarization (IMS) treats the problem as a sequence of single-document summarization tasks. Both approaches are implemented in our general framework driven by sentence compression. Instead of a purely extractive approach, we employ linguistic and statistical methods to generate multiple compressions, and then select from those candidates to produce a final summary. We demonstrate these ideas on the Enron email collection – a very challenging corpus because of the highly technical language. Experimental results point to two findings: that CMS represents a better approach to email thread summarization, and that current sentence compression techniques do not improve summarization performance in this genre. 相似文献
15.
Text summarization is a method for data reduction. The use of text summarization enables users to reduce the amount of text that must be read while still assimilating the core information. The data reduction offered by text summarization is particularly useful in the biomedical domain, where physicians must continuously find clinical trial study information to incorporate into their patient treatment efforts. Such efforts are often hampered by the high-volume of publications. This paper presents two independent methods (BioChain and FreqDist) for identifying salient sentences in biomedical texts using concepts derived from domain-specific resources. Our semantic-based method (BioChain) is effective at identifying thematic sentences, while our frequency-distribution method (FreqDist) removes information redundancy. The two methods are then combined to form a hybrid method (ChainFreq). An evaluation of each method is performed using the ROUGE system to compare system-generated summaries against a set of manually-generated summaries. The BioChain and FreqDist methods outperform some common summarization systems, while the ChainFreq method improves upon the base approaches. Our work shows that the best performance is achieved when the two methods are combined. The paper also presents a brief physician’s evaluation of three randomly-selected papers from an evaluation corpus to show that the author’s abstract does not always reflect the entire contents of the full-text. 相似文献
16.
《Information processing & management》2016,52(4):670-681
General graph random walk has been successfully applied in multi-document summarization, but it has some limitations to process documents by this way. In this paper, we propose a novel hypergraph based vertex-reinforced random walk framework for multi-document summarization. The framework first exploits the Hierarchical Dirichlet Process (HDP) topic model to learn a word-topic probability distribution in sentences. Then the hypergraph is used to capture both cluster relationship based on the word-topic probability distribution and pairwise similarity among sentences. Finally, a time-variant random walk algorithm for hypergraphs is developed to rank sentences which ensures sentence diversity by vertex-reinforcement in summaries. Experimental results on the public available dataset demonstrate the effectiveness of our framework. 相似文献
17.
《Information processing & management》2023,60(3):103291
Generating news headlines has been one of the predominant problems in Natural Language Processing research. Modern transformer models, if fine-tuned, can present a good headline with attention to all the parts of a disaster-news article. A disaster-news headline generally focuses on the event, its effect, and the major impacts, which a transformer model lacks when generating the headline. The extract-then-abstract based method proposed in this article improves the performance of a state-of-the-art transformer abstractor to generate a good-quality disaster-news headline. In this work, a Deep Neural Network (DNN) based sentence extractor and a transformer-based abstractive summarizer work sequentially to generate a headline. The sentence extraction task is formulated as a binary classification problem where the DNN model is trained to generate two binary labels: one corresponding to the sentence similarity with ground truth headlines and the other corresponding to the presence of disaster and its impact related information in the sentence. The transformer model generates the headline from the sentences extracted by the DNN. ROUGE scores of the headlines generated using the proposed method are found to be better than the scores of the headlines generated directly from the original documents. The highest ROUGE 1, 2, and 3 score improvements are observed in the case of the Text-To-Text Transfer Transformer (T5) model by 17.85%, 38.13%, and 21.01%, respectively. Such improvements suggest that the proposed method can have a high utility for finding effective headlines from disaster related news articles. 相似文献
18.
《Information processing & management》2023,60(1):103138
Opinion summarization can facilitate user’s decision-making by mining the salient review information. However, due to the lack of sufficient annotated data, most of the early works are based on extractive methods, which restricts the performance of opinion summarization. In this work, we aim to improve the informativeness of opinion summarization to provide better guidance to users. We consider the setting with only reviews without corresponding summaries, and propose an aspect-augmented model for unsupervised abstractive opinion summarization, denoted as AsU-OSum. We first employ an aspect-based sentiment analysis system to extract opinion phrases from reviews. Then, we construct a heterogeneous graph consisting of reviews and opinion clusters as nodes, which is used to enhance the Transformer-based encoder–decoder framework. Furthermore, we design a novel cascaded attention mechanism to prompt the decoder to pay more attention to the aspects that are more likely to appear in summary. During training, we introduce a sentiment accuracy reward that further enhances the learning ability of our model. We conduct comprehensive experiments on the Yelp, Amazon, and Rotten Tomatoes datasets. Automatic evaluation results show that our model is competitive and performs better than the state-of-the-art (SOTA) models on some ROUGE metrics. Human evaluation results further verify that our model can generate more informative summaries and reduce redundancy. 相似文献
19.
We argue that the quality of a summary can be evaluated based on how many concepts in the original document(s) that can be preserved after summarization. Here, a concept refers to an abstract or concrete entity or its action often expressed by diverse terms in text. Summary generation can thus be considered as an optimization problem of selecting a set of sentences with minimal answer loss. In this paper, we propose a document concept lattice that indexes the hierarchy of local topics tied to a set of frequent concepts and the corresponding sentences containing these topics. The local topics will specify the promising sub-spaces related to the selected concepts and sentences. Based on this lattice, the summary is an optimized selection of a set of distinct and salient local topics that lead to maximal coverage of concepts with the given number of sentences. Our summarizer based on the concept lattice has demonstrated competitive performance in Document Understanding Conference 2005 and 2006 evaluations as well as follow-on tests. 相似文献
20.
Yogesh Sankarasubramaniam Krishnan Ramanathan Subhankar Ghosh 《Information processing & management》2014
Automatic text summarization has been an active field of research for many years. Several approaches have been proposed, ranging from simple position and word-frequency methods, to learning and graph based algorithms. The advent of human-generated knowledge bases like Wikipedia offer a further possibility in text summarization – they can be used to understand the input text in terms of salient concepts from the knowledge base. In this paper, we study a novel approach that leverages Wikipedia in conjunction with graph-based ranking. Our approach is to first construct a bipartite sentence–concept graph, and then rank the input sentences using iterative updates on this graph. We consider several models for the bipartite graph, and derive convergence properties under each model. Then, we take up personalized and query-focused summarization, where the sentence ranks additionally depend on user interests and queries, respectively. Finally, we present a Wikipedia-based multi-document summarization algorithm. An important feature of the proposed algorithms is that they enable real-time incremental summarization – users can first view an initial summary, and then request additional content if interested. We evaluate the performance of our proposed summarizer using the ROUGE metric, and the results show that leveraging Wikipedia can significantly improve summary quality. We also present results from a user study, which suggests that using incremental summarization can help in better understanding news articles. 相似文献