Name
Abstract | ||
|---|---|---|
Extractive text or speech summarization endeavors to select representative sentences from a source document and assemble them into a concise summary, so as to help people to browse and assimilate the main theme of the document efficiently. The recent past has seen a surge of interest in developing deep learning- or deep neural network-based supervised methods for extractive text summarization. This paper presents a continuation of this line of research for speech summarization and its contributions are three-fold. First, we exploit an effective framework that integrates two convolutional neural networks (CNNs) and a multilayer perceptron (MLP) for summary sentence selection. Specifically, CNNs encode a given document-sentence pair into two discriminative vector embeddings separately, while MLP in turn takes the two embeddings of a document-sentence pair and their similarity measure as the input to induce a ranking score for each sentence. Second, the input of MLP is augmented by a rich set of prosodic and lexical features apart from those derived from CNNs. Third, the utility of our proposed summarization methods and several widely-used methods are extensively analyzed and compared. The empirical results seem to demonstrate the effectiveness of our summarization method in relation to several state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/SLT.2016.7846259 | 2016 IEEE Spoken Language Technology Workshop (SLT) |
Keywords | Field | DocType |
speech summarization,deep learning,deep neural network,convolutional neural network | Similarity measure,Computer science,Convolutional neural network,Multilayer perceptron,Artificial intelligence,Natural language processing,Deep learning,Artificial neural network,Discriminative model,Automatic summarization,Pattern recognition,Support vector machine,Speech recognition,Machine learning | Conference |
ISSN | ISBN | Citations |
2639-5479 | 978-1-5090-4904-2 | 1 |
PageRank | References | Authors |
0.36 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chun-I Tsai | 1 | 1 | 0.36 |
| Hsiao-Tsung Hung | 2 | 1 | 0.36 |
| Kuan-Yu Chen | 3 | 5 | 2.24 |
| Berlin Chen | 4 | 151 | 34.59 |