Lo Sau-Han Silvia. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 137-143). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Definition of a summary --- p.2 / Chapter 1.2 --- Definition of text summarization --- p.3 / Chapter 1.3 --- Previous work --- p.4 / Chapter 1.3.1 --- Extract-based text summarization --- p.5 / Chapter 1.3.2 --- Abstract-based text summarization --- p.8 / Chapter 1.3.3 --- Sophisticated text summarization --- p.9 / Chapter 1.4 --- Summarization evaluation methods --- p.10 / Chapter 1.4.1 --- Intrinsic evaluation --- p.10 / Chapter 1.4.2 --- Extrinsic evaluation --- p.11 / Chapter 1.4.3 --- The TIPSTER SUMMAC text summarization evaluation --- p.11 / Chapter 1.4.4 --- Text Summarization Challenge (TSC) --- p.13 / Chapter 1.5 --- Research contributions --- p.14 / Chapter 1.5.1 --- Text summarization based on thematic term approach --- p.14 / Chapter 1.5.2 --- Bilingual news summarization based on an event-driven approach --- p.15 / Chapter 1.6 --- Thesis organization --- p.16 / Chapter 2 --- Text Summarization based on a Thematic Term Approach --- p.17 / Chapter 2.1 --- System overview --- p.18 / Chapter 2.2 --- Document preprocessor --- p.20 / Chapter 2.2.1 --- English corpus --- p.20 / Chapter 2.2.2 --- English corpus preprocessor --- p.22 / Chapter 2.2.3 --- Chinese corpus --- p.23 / Chapter 2.2.4 --- Chinese corpus preprocessor --- p.24 / Chapter 2.3 --- Corpus thematic term extractor --- p.24 / Chapter 2.4 --- Article thematic term extractor --- p.26 / Chapter 2.5 --- Sentence score generator --- p.29 / Chapter 2.6 --- Chapter summary --- p.30 / Chapter 3 --- Evaluation for Summarization using the Thematic Term Ap- proach --- p.32 / Chapter 3.1 --- Content-based similarity measure --- p.33 / Chapter 3.2 --- Experiments using content-based similarity measure --- p.36 / Chapter 3.2.1 --- English corpus and parameter training --- p.36 / Chapter 3.2.2 --- Experimental results using content-based similarity mea- sure --- p.38 / Chapter 3.3 --- Average inverse rank (AIR) method --- p.59 / Chapter 3.4 --- Experiments using average inverse rank method --- p.60 / Chapter 3.4.1 --- Corpora and parameter training --- p.61 / Chapter 3.4.2 --- Experimental results using AIR method --- p.62 / Chapter 3.5 --- Comparison between the content-based similarity measure and the average inverse rank method --- p.69 / Chapter 3.6 --- Chapter summary --- p.73 / Chapter 4 --- Bilingual Event-Driven News Summarization --- p.74 / Chapter 4.1 --- Corpora --- p.75 / Chapter 4.2 --- Topic and event definitions --- p.76 / Chapter 4.3 --- Architecture of bilingual event-driven news summarization sys- tem --- p.77 / Chapter 4.4 --- Bilingual event-driven approach summarization --- p.80 / Chapter 4.4.1 --- Dictionary-based term translation applying on English news articles --- p.80 / Chapter 4.4.2 --- Preprocessing for Chinese news articles --- p.89 / Chapter 4.4.3 --- Event clusters generation --- p.89 / Chapter 4.4.4 --- Cluster selection and summary generation --- p.96 / Chapter 4.5 --- Evaluation for summarization based on event-driven approach --- p.101 / Chapter 4.6 --- Experimental results on event-driven summarization --- p.103 / Chapter 4.6.1 --- Experimental settings --- p.103 / Chapter 4.6.2 --- Results and analysis --- p.105 / Chapter 4.7 --- Chapter summary --- p.113 / Chapter 5 --- Applying Event-Driven Summarization to a Parallel Corpus --- p.114 / Chapter 5.1 --- Parallel corpus --- p.115 / Chapter 5.2 --- Parallel documents preparation --- p.116 / Chapter 5.3 --- Evaluation methods for the event-driven summaries generated from the parallel corpus --- p.118 / Chapter 5.4 --- Experimental results and analysis --- p.121 / Chapter 5.4.1 --- Experimental settings --- p.121 / Chapter 5.4.2 --- Results and analysis --- p.123 / Chapter 5.5 --- Chapter summary --- p.132 / Chapter 6 --- Conclusions and Future Work --- p.133 / Chapter 6.1 --- Conclusions --- p.133 / Chapter 6.2 --- Future work --- p.135 / Bibliography --- p.137 / Chapter A --- English Stop Word List --- p.144 / Chapter B --- Chinese Stop Word List --- p.149 / Chapter C --- Event List Items on the Corpora --- p.151 / Chapter C.1 --- "Event list items for the topic ""Upcoming Philippine election""" --- p.151 / Chapter C.2 --- "Event list items for the topic ""German train derail"" " --- p.153 / Chapter C.3 --- "Event list items for the topic ""Electronic service delivery (ESD) scheme"" " --- p.154 / Chapter D --- The sample of an English article (9505001.xml). --- p.156

Wang Fu Lee. / "August 2003." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (p. 256-281). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

by Tang Ting Kap. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 88-94). / Abstracts in English and Chinese. / List of Figures --- p.vi / List of Tables --- p.vii / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- Information Overloading and Low Utilization --- p.2 / Chapter 1.2 --- Problem Needs To Solve --- p.3 / Chapter 1.3 --- Research Contributions --- p.4 / Chapter 1.3.1 --- Using Concept Space in Summarization --- p.5 / Chapter 1.3.2 --- New Extraction Method --- p.5 / Chapter 1.3.3 --- Experiments on New System --- p.6 / Chapter 1.4 --- Organization of This Thesis --- p.7 / Chapter 2. --- LITERATURE REVIEW --- p.8 / Chapter 2.1 --- Classical Approach --- p.8 / Chapter 2.1.1 --- Luhn's Algorithm --- p.9 / Chapter 2.1.2 --- Edumundson's Algorithm --- p.11 / Chapter 2.2 --- Statistical Approach --- p.15 / Chapter 2.3 --- Natural Language Processing Approach --- p.15 / Chapter 3. --- PROPOSED SUMMARIZATION APPROACH --- p.18 / Chapter 3.1 --- Direction of Summarization --- p.19 / Chapter 3.2 --- Overview of Summarization Algorithm --- p.20 / Chapter 3.2.1 --- Document Pre-processing --- p.21 / Chapter 3.2.2 --- Vector Space Model --- p.23 / Chapter 3.2.3 --- Sentence Extraction --- p.24 / Chapter 3.3 --- Evaluation Method --- p.25 / Chapter 3.3.1 --- "Recall, Precision and F-measure" --- p.25 / Chapter 3.4 --- Advantage of Concept Space Approach --- p.26 / Chapter 4. --- SYSTEM ARCHITECTURE --- p.27 / Chapter 4.1 --- Converge Process --- p.28 / Chapter 4.2 --- Diverge Process --- p.30 / Chapter 4.3 --- Backward Search --- p.31 / Chapter 5. --- CONVERGE PROCESS --- p.32 / Chapter 5.1 --- Document Merging --- p.32 / Chapter 5.2 --- Word Phrase Extraction --- p.34 / Chapter 5.3 --- Automatic Indexing --- p.34 / Chapter 5.4 --- Cluster Analysis --- p.35 / Chapter 5.5 --- Hopfield Net Classification --- p.37 / Chapter 6. --- DIVERGE PROCESS --- p.42 / Chapter 6.1 --- Concept Terms Refinement --- p.42 / Chapter 6.2 --- Sentence Selection --- p.43 / Chapter 6.3 --- Backward Searching --- p.46 / Chapter 7. --- EXPERIMENT AND RESEARCH FINDINGS --- p.48 / Chapter 7.1 --- System-generated Summary v.s. Source Documents --- p.52 / Chapter 7.1.1 --- Compression Ratio --- p.52 / Chapter 7.1.2 --- Information Loss --- p.54 / Chapter 7.2 --- System-generated Summary v.s. Human-generated Summary --- p.58 / Chapter 7.2.1 --- Background of EXTRACTOR --- p.59 / Chapter 7.2.2 --- Evaluation Method --- p.61 / Chapter 7.3 --- Evaluation of different System-generated Summaries by Human Experts --- p.63 / Chapter 8. --- CONCLUSIONS AND FUTURE RESEARCH --- p.68 / Chapter 8.1 --- Conclusions --- p.68 / Chapter 8.2 --- Future Work --- p.69 / Chapter A. --- EXTRACTOR SYSTEM FLOW AND TEN-STEP PROCEDURE --- p.71 / Chapter B. --- SUMMARY GENERATED BY MS WORD2000 --- p.75 / Chapter C. --- SUMMARY GENERATED BY EXTRACTOR SOFTWARE --- p.76 / Chapter D. --- SUMMARY GENERATED BY OUR SYSTEM --- p.77 / Chapter E. --- SYSTEM-GENERATED WORD PHRASES FROM TEST SAMPLE --- p.78 / Chapter F. --- WORD PHRASES IDENTIFIED BY SUBJECTS --- p.79 / Chapter G. --- SAMPLE OF QUESTIONNAIRE --- p.84 / Chapter H. --- RESULT OF QUESTIONNAIRE --- p.85 / Chapter I. --- EVALUATION FOR DIVERGE PROCESS --- p.86 / BIBLIOGRAPHY --- p.88

Lu, Shi. / Thesis submitted in: December 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 100-109). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Our Contributions --- p.3 / Chapter 1.3 --- Thesis Outline --- p.4 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Static Video Summary --- p.6 / Chapter 2.2 --- Dynamic Video Skimming --- p.10 / Chapter 2.3 --- Summary --- p.14 / Chapter 3 --- Greedy Method Based Skim Generation --- p.16 / Chapter 3.1 --- Selected Video Features for Video Summarization --- p.17 / Chapter 3.2 --- Video Summarization Problem --- p.18 / Chapter 3.3 --- Experiments --- p.22 / Chapter 3.4 --- Summary --- p.25 / Chapter 4 --- Video Structure Analysis --- p.27 / Chapter 4.1 --- Video Shot Detection --- p.29 / Chapter 4.1.1 --- Shot Cut Detection --- p.30 / Chapter 4.1.2 --- Fade Detection --- p.35 / Chapter 4.2 --- Video Shot Group Construction --- p.38 / Chapter 4.2.1 --- Shot Pairwise Similarity Measure --- p.39 / Chapter 4.2.2 --- Video Shot Grouping by VToC --- p.41 / Chapter 4.2.3 --- Spectral Graph Partitioning --- p.42 / Chapter 4.3 --- Video Scene Detection --- p.46 / Chapter 4.4 --- Shot Arrangement Patterns --- p.48 / Chapter 4.5 --- Experiments --- p.50 / Chapter 4.6 --- Summary --- p.53 / Chapter 5 --- Graph Optimization-Based Video Summary Generation --- p.55 / Chapter 5.1 --- Video Scene Analysis --- p.56 / Chapter 5.1.1 --- Scene Content Entropy --- p.57 / Chapter 5.1.2 --- Target Skim Length Assignment --- p.58 / Chapter 5.2 --- Graph Modelling of Video Scenes --- p.59 / Chapter 5.2.1 --- Decompose the Video Scene into Candidate Video Strings --- p.60 / Chapter 5.2.2 --- The Spatial-Temporal Relation Graph --- p.61 / Chapter 5.2.3 --- The Optimal Skim Problem --- p.62 / Chapter 5.3 --- Graph Optimization --- p.64 / Chapter 5.4 --- Static Video Summary Generation --- p.65 / Chapter 5.5 --- Experiments --- p.68 / Chapter 5.6 --- Summary --- p.74 / Chapter 6 --- Video Content Annotation and Semantic Video Summarization --- p.75 / Chapter 6.1 --- Semantic Video Content Annotation --- p.77 / Chapter 6.1.1 --- Video Shot Segmentation --- p.77 / Chapter 6.1.2 --- Semi-Automatic Video Shot Annotation --- p.77 / Chapter 6.2 --- Video Structures and Semantics --- p.78 / Chapter 6.2.1 --- Video Structure Analysis --- p.78 / Chapter 6.2.2 --- Video Structure and Video Edit Process --- p.80 / Chapter 6.2.3 --- Mutual Reinforcement and Semantic Video Shot Group Detection --- p.81 / Chapter 6.3 --- Semantic Video Summarization --- p.84 / Chapter 6.3.1 --- Summarization Requests and Goals --- p.84 / Chapter 6.3.2 --- Determine the Sub-Skimming Length for Each Scene --- p.85 / Chapter 6.3.3 --- Extracting Video Shots by String Analysis --- p.86 / Chapter 6.4 --- Experiments --- p.88 / Chapter 6.5 --- Summary --- p.92 / Chapter 7 --- Concluding Remarks --- p.93 / Chapter 7.1 --- Summary --- p.93 / Chapter 7.2 --- Future Work --- p.95 / Chapter A --- Notations --- p.97 / Bibliography --- p.100

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 105-107).

Summarization is a complex task that requires understanding of the document content to determine the importance of the text. Lexical cohesion is a method to identify connected portions of the text based on the relations between the words in the text. Lexical cohesive relations can be represented using lexical chaings. Lexical chains are sequences of semantically related words spread over the entire text. Lexical chains are used in variety of Natural Language Processing (NLP) and Information Retrieval (IR) applications. In current thesis, we propose a lexical chaining method that includes the glossary relations in the chaining process. These relations enable us to identify topically related concepts, for instance dormitory and student, and thereby enhances the identification of cohesive ties in the text. We then present methods that use the lexical chains to generate summaries by extracting sentences from the document(s). Headlines are generated by filtering the portions of the sentences extracted, which do not contribute towards the meaning of the sentence. Headlines generated can be used in real world application to skim through the document collections in a digital library. Multi-document summarization is gaining demand with the explosive growth of online news sources. It requires identification of the several themes present in the collection to attain good compression and avoid redundancy. In this thesis, we propose methods to group the portions of the texts of a document collection into meaningful clusters. clustering enable us to extract the various themes of the document collection. Sentences from clusters can then be extracted to generate a summary for the multi-document collection. Clusters can also be used to generate summaries with respect to a given query. We designed a system to compute lexical chains for the given text and use them to extract the salient portions of the document. Some specific tasks considered are: headline generation, multi-document summarization, and query-based summarization. Our experimental evaluation shows that efficient summaries can be extracted for the above tasks. / viii, 80 leaves : ill. ; 29 cm.

Univ., Diss.--Leuven, 1999. / Includes bibliographical references (p. [237] - 260) and index.

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

A digital library is a collection of services and information objects for storing, accessing, and retrieving digital objects. Automatic text summarization presents salient information in a condensed form suitable for user needs. This thesis amalgamates digital libraries and automatic text summarization by extending the Greenstone Digital Library software suite to include the University of Lethbridge Summarizer. The tool generates summaries, nouns, and non phrases for use as metadata for searching and browsing digital collections. Digital collections of newspapers, PDFs, and eBooks were created with summary metadata. PDF documents were processed the fastest at 1.8 MB/hr, followed by the newspapers at 1.3 MB/hr, with eBooks being the slowest at 0.9 MV/hr. Qualitative analysis on four genres: newspaper, M.Sc. thesis, novel, and poetry, revealed narrative newspapers were most suitable for automatically generated summarization. The other genres suffered from incoherence and information loss. Overall, summaries for digital collections are suitable when used with newspaper documents and unsuitable for other genres. / xiii, 142 leaves ; 28 cm.

Indiana University-Purdue University Indianapolis (IUPUI) / Surveillance videos are recorded continually and the retrieval of such videos currently still relies on human operators. Automatic retrieval has not reached a satisfactory accuracy. As an intermediate representation, this work develops multiple original temporal profiles of video to convey accurate temporal information in the video while keeping certain spatial characteristics. These are effective methods to visualizes surveillance video contents efficiently in a 2D temporal image, suitable for indexing and retrieving a large video database. We are aiming to provide a compact index that is intuitive and preserves most of the information in the video in order to avoid browsing extensive video clips frame by frame. By considering some of the properties of static surveillance videos, we aim at accentuating the temporal dimension in our visualization. We have introduced our framework as three unique methods that visualize different aspects of a surveillance video, plus an extension to non-static surveillance videos. In our first method "Localized Temporal Profile", by knowing that most surveillance videos are monitoring specific locations, we try to emphasize the other dimension, time, in our solution. we focus on describing all the events only in critical locations of the video. In our next method "Multi-Position Temporal Profile", we generate an all-inclusive profile that covers all the events in the video field of view. In our last method "Motion Temporal Profile" we perform in-depth analysis of scene motion and try to handle targets with non-uniform, non-translational motion in our temporal profile. We then further extend our framework by loosening the constraint that the video is static and including cameras with smooth panning motion as such videos are widely used in practice. By performing motion analysis on the camera, we stabilize the camera to create a panorama-like effect for the video, allowing us to utilize all of the aforementioned methods. The resulting profiles allows temporal indexing to each video frame, and contains all spatial information in a continuous manner. It also shows the actions and progress of events in the temporal profile. Flexible browsing and effective manipulation of videos can be achieved using the resulting video profiles.