This paper presents a fully automatic, multi-stage compaction approach to broadcast news summarisation, targeting transcripts from automatic speech recognition (ASR) systems. It employs a network of multi-layer perceptrons to remove incorrectly transcribed words based on confidence scores, and to select significant chunks at multiple stages based on tf.idf scores and named entity frequency. The resulting summaries are assessed using a combination of cross comprehension test and a fluency test, finally compared with an automatic evaluation scheme. The experimental results show the approach can produce summaries with good information content.
Cite as: Kolluru, B., Christensen, H., Gotoh, Y. (2005) Multi-stage compaction approach to broadcast news summarisation. Proc. Interspeech 2005, 69-72, doi: 10.21437/Interspeech.2005-56
@inproceedings{kolluru05_interspeech,
author={BalaKrishna Kolluru and Heidi Christensen and Yoshihiko Gotoh},
title={{Multi-stage compaction approach to broadcast news summarisation}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={69--72},
doi={10.21437/Interspeech.2005-56}
}