The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
HLT '01 Proceedings of the first international conference on Human language technology research
Supervised ranking in open-domain text summarization
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th international conference on Software engineering
Detection of question-answer pairs in email conversations
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Detection of Duplicate Defect Reports Using Natural Language Processing
ICSE '07 Proceedings of the 29th international conference on Software Engineering
How Long Will It Take to Fix This Bug?
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
An approach to detecting duplicate bug reports using natural language and execution information
Proceedings of the 30th international conference on Software engineering
Predicting diverse subsets using structural SVMs
Proceedings of the 25th international conference on Machine learning
Finding question-answer pairs from online forums
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Enhancing diversity, coverage and balance for summarization through structure learning
Proceedings of the 18th international conference on World wide web
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Summarization of large scale social network activity
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Extractive summarization using supervised and semi-supervised learning
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Summarizing spoken and written conversations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Generating and evaluating evaluative arguments
Artificial Intelligence
Summarization from medical documents: a survey
Artificial Intelligence in Medicine
The automatic creation of literature abstracts
IBM Journal of Research and Development
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Summarizing software artifacts: a case study of bug reports
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
DivRank: the interplay of prestige and diversity in information networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards automatically generating summary comments for Java methods
Proceedings of the IEEE/ACM international conference on Automated software engineering
Personalized video summarization with human in the loop
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Automatically detecting and describing high level actions within methods
Proceedings of the 33rd International Conference on Software Engineering
Diversity in ranking via resistive graph centers
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy protected knowledge management in services with emphasis on quality data
Proceedings of the 20th ACM international conference on Information and knowledge management
The AMI meeting corpus: a pre-announcement
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Bug resolution catalysts: identifying essential non-committers from bug repositories
Proceedings of the 10th Working Conference on Mining Software Repositories
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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In most software projects, resolved bugs are archived for future reference. These bug reports contain valuable information on the reported problem, investigation and resolution. When bug triaging, developers look for how similar problems were resolved in the past. Search over bug repository gives the developer a set of recommended bugs to look into. However, the developer still needs to manually peruse the contents of the recommended bugs which might vary in size from a couple of lines to thousands. Automatic summarization of bug reports is one way to reduce the amount of data a developer might need to go through. Prior work has presented learning based approaches for bug summarization. These approaches have the disadvantage of requiring large training set and being biased towards the data on which the model was learnt. In fact, maximum efficacy was reported when the model was trained and tested on bug reports from the same project. In this paper, we present the results of applying four unsupervised summarization techniques for bug summarization. Industrial bug reports typically contain a large amount of noise---email dump, chat transcripts, core-dump---useless sentences from the perspective of summarization. These derail the unsupervised approaches, which are optimized to work on more well-formed documents. We present an approach for noise reduction, which helps to improve the precision of summarization over the base technique (4% to 24% across subjects and base techniques). Importantly, by applying noise reduction, two of the unsupervised techniques became scalable for large sized bug reports.