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Table of Contents
Intro; Preface; Contents; About the Authors; 1 Introduction; 1.1 Book Organisation; 1.2 Types of Summarisation Techniques; 1.3 Extractive Summarisation; 1.4 Information Fusion and Ensemble Techniques; 1.5 Abstractive Summarisation; 1.6 Main Contributions; References; 2 Related Work; 2.1 Extractive Summarisation; 2.2 Ensemble Techniques for Extractive Summarisation; 2.3 Sentence Compression; 2.4 Domain-Specific Summarisation; 2.4.1 Legal Document Summarisation; 2.4.2 Scientific Article Summarisation; References; 3 Corpora and Evaluation for Text Summarisation; 3.1 DUC and TAC Datasets
3.2 Legal and Scientific Article Dataset3.3 Evaluation; 3.3.1 Precision and Recall; 3.3.2 BLEU; 3.3.3 ROUGE Measure; 3.3.4 Pyramid Score; 3.3.5 Human Evaluation; References; 4 Domain-Specific Summarisation; 4.1 Legal Document Summarisation; 4.1.1 Boosting Legal Vocabulary Using a Lexicon; 4.1.2 Weighted TextRank and LexRank; 4.1.3 Automatic Keyphrase Identification; 4.1.4 Attention-Based Sentence Extractor; 4.2 Scientific Article Summarisation; 4.3 Experiment Details; 4.3.1 Results; 4.4 Conclusion; References; 5 Improving Sentence Extraction Through Rank Aggregation; 5.1 Introduction
5.2 Motivation for Rank Aggregation5.3 Analysis of Existing Extractive Systems; 5.3.1 Experimental Setup; 5.4 Ensemble of Extractive Summarisation Systems; 5.4.1 Effect of Informed Fusion; 5.5 Discussion; 5.5.1 Determining the Robustness of Candidate Systems; 5.5.2 Qualitative Analysis of Summaries; References; 6 Leveraging Content Similarity in Summaries for Generating Better Ensembles; 6.1 Limitations of Consensus-Based Aggregation; 6.2 Proposed Approach for Content-Based Aggregation; 6.3 Document Level Aggregation; 6.3.1 Experimental Results; 6.4 Sentence Level Aggregation; 6.4.1 SentRank
6.4.2 GlobalRank6.4.3 LocalRank; 6.4.4 HybridRank; 6.4.5 Experimental Results; 6.5 Conclusion; References; 7 Neural Model for Sentence Compression; 7.1 Sentence Compression by Deletion; 7.2 Sentence Compression Using Sequence to Sequence Model; 7.2.1 Sentence Encoder; 7.2.2 Context Encoder; 7.2.3 Decoder; 7.2.4 Attention Module; 7.3 Exploiting SMT Techniques for Sentence Compression; 7.4 Results for Sentence Compression; 7.5 Limitations of Sentence Compression Techniques; 7.6 Overall System; References; 8 Conclusion; References; A Sample Document-Summary Pairs from DUC, Legal and ACL Corpus
B The Dictionary Built Using Legal Boost MethodC Summaries Generated Using Rank Aggregation; D Summaries Generated Using Content-Based Aggregation; E Visualising Compression on Sentences from Legal Documents
3.2 Legal and Scientific Article Dataset3.3 Evaluation; 3.3.1 Precision and Recall; 3.3.2 BLEU; 3.3.3 ROUGE Measure; 3.3.4 Pyramid Score; 3.3.5 Human Evaluation; References; 4 Domain-Specific Summarisation; 4.1 Legal Document Summarisation; 4.1.1 Boosting Legal Vocabulary Using a Lexicon; 4.1.2 Weighted TextRank and LexRank; 4.1.3 Automatic Keyphrase Identification; 4.1.4 Attention-Based Sentence Extractor; 4.2 Scientific Article Summarisation; 4.3 Experiment Details; 4.3.1 Results; 4.4 Conclusion; References; 5 Improving Sentence Extraction Through Rank Aggregation; 5.1 Introduction
5.2 Motivation for Rank Aggregation5.3 Analysis of Existing Extractive Systems; 5.3.1 Experimental Setup; 5.4 Ensemble of Extractive Summarisation Systems; 5.4.1 Effect of Informed Fusion; 5.5 Discussion; 5.5.1 Determining the Robustness of Candidate Systems; 5.5.2 Qualitative Analysis of Summaries; References; 6 Leveraging Content Similarity in Summaries for Generating Better Ensembles; 6.1 Limitations of Consensus-Based Aggregation; 6.2 Proposed Approach for Content-Based Aggregation; 6.3 Document Level Aggregation; 6.3.1 Experimental Results; 6.4 Sentence Level Aggregation; 6.4.1 SentRank
6.4.2 GlobalRank6.4.3 LocalRank; 6.4.4 HybridRank; 6.4.5 Experimental Results; 6.5 Conclusion; References; 7 Neural Model for Sentence Compression; 7.1 Sentence Compression by Deletion; 7.2 Sentence Compression Using Sequence to Sequence Model; 7.2.1 Sentence Encoder; 7.2.2 Context Encoder; 7.2.3 Decoder; 7.2.4 Attention Module; 7.3 Exploiting SMT Techniques for Sentence Compression; 7.4 Results for Sentence Compression; 7.5 Limitations of Sentence Compression Techniques; 7.6 Overall System; References; 8 Conclusion; References; A Sample Document-Summary Pairs from DUC, Legal and ACL Corpus
B The Dictionary Built Using Legal Boost MethodC Summaries Generated Using Rank Aggregation; D Summaries Generated Using Content-Based Aggregation; E Visualising Compression on Sentences from Legal Documents