@InProceedings{7362615,
  author = {M. H. Bokaetf and H. Sameti and Y. Liu},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Unsupervised approach to extract summary keywords in meeting domain},
  year = {2015},
  pages = {1406-1410},
  abstract = {Summary keywords are words that are used in the reference extracted summary, therefore can be used to discriminate between summary sentences from non-summary ones. Finding these words is important for the extractive summarization algorithms that measure the importance of a sentence based on the importance of its constituent words. This paper is focused on extracting summary keywords in the multi-party meeting domain. We test previously proposed keyword extraction algorithms and evaluate their performance to determine summary keywords. We also propose a new approach which uses discourse information to find local important keywords and show that it outperforms all the previous methods. We evaluate our proposed approach on the standard AMI meeting corpus according to the reference extracted summary prepared in this corpus.},
  keywords = {information retrieval;unsupervised learning;unsupervised learning approach;summary keyword extraction;extractive summarization algorithms;multiparty meeting domain;discourse information;AMI meeting corpus;Signal processing algorithms;Feature extraction;Europe;Signal processing;Market research;Speech;Standards;meeting segmentation;funtion segmentation;unsupervised algorithm;summary keyword extraction},
  doi = {10.1109/EUSIPCO.2015.7362615},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104351.pdf},
}

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Finding these words is important for the extractive summarization algorithms that measure the importance of a sentence based on the importance of its constituent words. This paper is focused on extracting summary keywords in the multi-party meeting domain. We test previously proposed keyword extraction algorithms and evaluate their performance to determine summary keywords. We also propose a new approach which uses discourse information to find local important keywords and show that it outperforms all the previous methods. We evaluate our proposed approach on the standard AMI meeting corpus according to the reference extracted summary prepared in this corpus.","keywords":"information retrieval;unsupervised learning;unsupervised learning approach;summary keyword extraction;extractive summarization algorithms;multiparty meeting domain;discourse information;AMI meeting corpus;Signal processing algorithms;Feature extraction;Europe;Signal processing;Market research;Speech;Standards;meeting segmentation;funtion segmentation;unsupervised algorithm;summary keyword extraction","doi":"10.1109/EUSIPCO.2015.7362615","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104351.pdf","bibtex":"@InProceedings{7362615,\n author = {M. H. Bokaetf and H. Sameti and Y. Liu},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Unsupervised approach to extract summary keywords in meeting domain},\n year = {2015},\n pages = {1406-1410},\n abstract = {Summary keywords are words that are used in the reference extracted summary, therefore can be used to discriminate between summary sentences from non-summary ones. Finding these words is important for the extractive summarization algorithms that measure the importance of a sentence based on the importance of its constituent words. This paper is focused on extracting summary keywords in the multi-party meeting domain. We test previously proposed keyword extraction algorithms and evaluate their performance to determine summary keywords. We also propose a new approach which uses discourse information to find local important keywords and show that it outperforms all the previous methods. 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