1 Introduction1.1 Extractive Summarization
1.2 Information Fusion and Ensemble Techniques
1.3 Abstractive Summarization
1.4 Main contributions
1.5 Organization
2 Related Work
2.1 Extractive Summarization
2.1.1 Legal Document Summarization
2.1.2 Scientific article Summarization
2.2 Ensemble techniques for extractive summarization
2.3 Sentence compression
3 Domain specific Extractive Summarization
3.1 Corpora
3.2 Legal document Summarization
3.2.1 Boosting legal vocabulary using a lexicon
3.2.2 Weighted TextRank and LexRank
3.2.3 Automatic key phrase identification
3.2.4 Attention based sentence extractor
3.3 Scientific article summarization
3.4 Experiment Details
3.4.1 Results
3.5 Conclusion
4 Improving extractive techniques through rank aggregation
4.1 Motivation for rank aggregation
4.2 Analysis of existing extractive systems
4.2.1 Experimental Setup
4.3 Ensemble of extractive summarization systems
4.3.1 Effect of Informed fusion
4.4 Discussion
4.4.1 Determining the robustness of candidate systems
4.4.2 Qualitative analysis of summaries
5 Leveraging content similarity in summaries for generating better ensembles
5.1 Limitations of consensus based aggregation
5.2 Proposed approach for content based aggregation
5.3 Document level aggregation
5.3.1 Experimental results
5.4 Sentence Level aggregation
5.4.1 SentRank
5.4.2 GlobalRank
5.4.3 LocalRank
5.4.4 HybridRank
5.4.5 Experimental Results
5.5 Conclusion
6 Neural model for sentence compression
6.1 Sentence compression by deletion
6.2 Sentence compression using Sequence to Sequence model
6.2.1 Sentence Encoder
6.2.2 Context Encoder
6.2.3 Decoder
6.2.4 Attention module
6.3 Exploiting SMT techniques for sentence compression
6.4 Results for sentence compression
6.5 Limitations of sentence compression techniques
6.6 Overall System
7 Conclusion and Future Work