Using maximum entropy for sentence extraction

@inproceedings{Osborne2002UsingME,
  title={Using maximum entropy for sentence extraction},
  author={M. Osborne},
  booktitle={ACL 2002},
  year={2002}
}
A maximum entropy classifier can be used to extract sentences from documents. Experiments using technical documents show that such a classifier tends to treat features in a categorical manner. This results in performance that is worse than when extracting sentences using a naive Bayes classifier. Addition of an optimised prior to the maximum entropy classifier improves performance over and above that of naive Bayes (even when naive Bayes is also extended with a similar prior). Further… Expand
147 Citations
Summarization with a Joint Model for Sentence Extraction and Compression
Combining Optimal Clustering and Hidden Markov Models for Extractive Summarization
An map based sentence ranking approach to automatic summarization
  • Xiaofeng Wu, Chengqing Zong
  • Computer Science
  • Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)
  • 2010
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 20 REFERENCES
Using Maximum Entropy for Text Classification
A Maximum-Entropy-Inspired Parser
A Maximum Entropy Approach to Natural Language Processing
Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger
A Gaussian Prior for Smoothing Maximum Entropy Models
Summarizing text documents: sentence selection and evaluation metrics
The automatic construction of large-scale corpora for summarization research
A trainable document summarizer
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
...
1
2
...