Discourse oriented summarization

The meaning of text appears to be tightly related to intentions and circumstances. Context sensitivity of meaning is addressed by theories of discourse structure. Few attempts have been made to exploit text organization in summarization. This thesis is an exploration of what knowledge of discourse structure can do for content selection as a subtask of automatic summarization, and query-based summarization in particular. Query-based summarization is the task of answering an arbitrary user query or question by using content from potentially relevant sources. This thesis presents a general framework for discourse oriented summarization, relying on graphs to represent semantic relations in discourse, and redundancy as a special type of semantic relation. Semantic relations occur on several levels of text analysis (query-relevance, coherence, layout, etc.), and a broad range of textual features may be required to detect them. The graph-based framework facilitates combining multiple features into an integrated semantic model of the documents to summarize. Recognizing redundancy and entailment relations between text passages is particularly important when a summary is generated of multiple documents, e.g. to avoid including redundant content in a summary. For this reason, I pay particular attention to recognizing textual entailment. Within this framework, a three-fold evaluation is performed to evaluate different aspects of discourse oriented summarization. The first is a user study, measuring the effect on user appreciation of using a particular type of knowledge for query-based summarization. In this study, three presentation strategies are compared: summarization using the rhetorical structure of the source, a baseline summarization method which uses the layout of the source, and a baseline presentation method which uses no summarization but just a concise answer to the query. Results show that knowledge of the rhetorical structure not only helps to provide the necessary context for the user to verify that the summary addresses the query adequately, but also to increase the amount of relevant content. The second evaluation is a comparison of implementations of the graph-based framework which are capable of fully automatic summarization. The two variables in the experiment are the set of textual features used to model the source and the algorithm used to search a graph for relevant content. The features are based on cosine similarity, and are realized as graph representations of the source. The graph search algorithms are inspired by existing algorithms in summarization. The quality of summaries is measured using the Rouge evaluation toolkit. The best performer would have ranked first (Rouge-2) or second (Rouge-SU4) if it had participated in the DUC 2005 query-based summarization challenge. The third study is an evaluation in the context of the DUC 2006 summarization challenge, which includes readability measurements as well as various content-based evaluation metrics. The evaluated automatic discourse oriented summarization system is similar to the one described above, but uses additional features, i.e. layout and textual entailment. The system performed well on readability at the cost of content-based scores which were well below the scores of the highest ranking DUC 2006 participant. This indicates a trade-off between readable, coherent content and useful content, an issue yet to be explored. Previous research implies that theories of text organization generalize well to multimedia. This suggests that the discourse oriented summarization framework applies to summarizing multimedia as well, provided sufficient knowledge of the organization of the (multimedia) source documents is available. The last study in this thesis is an investigation of the applicability of structural relations in multimedia for generating picture-illustrated summaries, by relating summary content to picture-associated text (i.e. captions or surrounding paragraphs). Results suggest that captions are the more suitable annotation for selecting appropriate pictures. Compared to manual illustration, results of automatic pictures are similar if the manual picture is mainly decorative

Automatic Document Summarization Using Knowledge Based System

This dissertation describes a knowledge-based system to create abstractive summaries of documents by generalizing new concepts, detecting main topics and creating new sentences. The proposed system is built on the Cyc development platform that consists of the world’s largest knowledge base and one of the most powerful inference engines. The system is unsupervised and domain independent. Its domain knowledge is provided by the comprehensive ontology of common sense knowledge contained in the Cyc knowledge base. The system described in this dissertation generates coherent and topically related new sentences as a summary for a given document. It uses syntactic structure and semantic features of the given documents to fuse information. It makes use of the knowledge base as a source of domain knowledge. Furthermore, it uses the reasoning engine to generalize novel information. The proposed system consists of three main parts: knowledge acquisition, knowledge discovery, and knowledge representation. Knowledge acquisition derives syntactic structure of each sentence in the document and maps words and their syntactic relationships into Cyc knowledge base. Knowledge discovery abstracts novel concepts, not explicitly mentioned in the document by exploring the ontology of mapped concepts and derives main topics described in the document by clustering the concepts. Knowledge representation creates new English sentences to summarize main concepts and their relationships. The syntactic structure of the newly created sentences is extended beyond simple subject-predicate-object triplets by incorporating adjective and adverb modifiers. This structure allows the system to create sentences that are more complex. The proposed system was implemented and tested. Test results show that the system is capable of creating new sentences that include abstracted concepts not mentioned in the original document and is capable of combining information from different parts of the document text to compose a summary

Extractive summarization using complex networks and syntactic dependency

AbstractThe realization that statistical physics methods can be applied to analyze written texts represented as complex networks has led to several developments in natural language processing, including automatic summarization and evaluation of machine translation. Most importantly, so far only a few metrics of complex networks have been used and therefore there is ample opportunity to enhance the statistics-based methods as new measures of network topology and dynamics are created. In this paper, we employ for the first time the metrics betweenness, vulnerability and diversity to analyze written texts in Brazilian Portuguese. Using strategies based on diversity metrics, a better performance in automatic summarization is achieved in comparison to previous work employing complex networks. With an optimized method the Rouge score (an automatic evaluation method used in summarization) was 0.5089, which is the best value ever achieved for an extractive summarizer with statistical methods based on complex networks for Brazilian Portuguese. Furthermore, the diversity metric can detect keywords with high precision, which is why we believe it is suitable to produce good summaries. It is also shown that incorporating linguistic knowledge through a syntactic parser does enhance the performance of the automatic summarizers, as expected, but the increase in the Rouge score is only minor. These results reinforce the suitability of complex network methods for improving automatic summarizers in particular, and treating text in general

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

Generating indicative-informative summaries with SumUM

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies

LCSTS: A Large Scale Chinese Short Text Summarization Dataset

Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public {http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. We also manually tagged the relevance of 10,666 short summaries with their corresponding short texts. Based on the corpus, we introduce recurrent neural network for the summary generation and achieve promising results, which not only shows the usefulness of the proposed corpus for short text summarization research, but also provides a baseline for further research on this topic.Comment: Recently, we received feedbacks from Yuya Taguchi from NAIST in Japan and Qian Chen from USTC of China, that the results in the EMNLP2015 version seem to be underrated. So we carefully checked our results and find out that we made a mistake while using the standard ROUGE. Then we re-evaluate all methods in the paper and get corrected results listed in Table 2 of this versio