An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features

• URL: http://arxiv.org/abs/2006.01189v1
• Date: Mon, 1 Jun 2020 18:27:48 GMT
• Title: An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features
• Authors: Shi-Yan Weng, Tien-Hong Lo, Berlin Chen
• Abstract summary: Bidirectional Representations from Transformers (BERT) model was proposed and has achieved record-breaking success on many natural language processing tasks. We explore the incorporation of confidence scores into sentence representations to see if such an attempt could help alleviate the negative effects caused by imperfect automatic speech recognition. We validate the effectiveness of our proposed method on a benchmark dataset.
• Score: 13.97006782398121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tremendous amounts of multimedia associated with speech information are driving an urgent need to develop efficient and effective automatic summarization methods. To this end, we have seen rapid progress in applying supervised deep neural network-based methods to extractive speech summarization. More recently, the Bidirectional Encoder Representations from Transformers (BERT) model was proposed and has achieved record-breaking success on many natural language processing (NLP) tasks such as question answering and language understanding. In view of this, we in this paper contextualize and enhance the state-of-the-art BERT-based model for speech summarization, while its contributions are at least three-fold. First, we explore the incorporation of confidence scores into sentence representations to see if such an attempt could help alleviate the negative effects caused by imperfect automatic speech recognition (ASR). Secondly, we also augment the sentence embeddings obtained from BERT with extra structural and linguistic features, such as sentence position and inverse document frequency (IDF) statistics. Finally, we validate the effectiveness of our proposed method on a benchmark dataset, in comparison to several classic and celebrated speech summarization methods.

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