- 學位論文
課程錄音之自動關鍵用語擷取及摘要
Automatic Key Term Extraction and Summarization from Spoken Course Lectures
摘要
本論文針對語音文件提出能夠自動擷取重要資訊的方法，其資訊包含關鍵用語及摘要。首先，本論文提出一有效擷取關鍵用語的方法，並以課程錄音做為實驗語料，其中包含了語音訊號、錄音文本、以及課程所使用之投影片。我們將關鍵用語分成兩類型：關鍵片語及關鍵詞，並對兩種類型之用語發展不同的擷取方法，並依序擷取兩種類型之關鍵用語。首先使用分歧亂度擷取關鍵片語，接著使用韻律、詞彙、語意三大類的特徵藉由機器學習來訓練分類器並抽取關鍵詞，其中機器學習包含一個非督導式學習( K 平均模範) 以及兩個督導式學習(調適性推昇法及類神經網路) 的方法。實驗顯示本論文提出之關鍵用語擷取方法明顯較傳統方法好，而且三大類不同的特徵對於關鍵用語之選擇皆有幫助並具有加成性。有了關鍵用語的資訊，本論文進一步提出使用圖論之隨機漫步演算法為此類語音文件自動抽取摘要。在此演算法中，文件中的每個句子被表示成一圖上的點，兩點相連邊的權重是根據兩句子間的主題相似度所估算的。基本的概念為，假設若一句子和較多重要的句子潛藏語意上較相似，則此句子應該較為重要。此方法藉由關鍵用語的資訊來輔助估算語音文件中每個句子的重要性，並藉由圖論的方法來全面性地考慮所有句子彼此間的相似性，進而重新估算各句子的重要性。實驗結果顯示，不論在何種評估方法下，此方法所抽取出的摘要都較傳統基於潛藏主題亂度所抽取之摘要更好，而關鍵用語的資訊及隨機漫步演算法都對語音文件中的摘要抽取具有極大的貢獻。
並列摘要
This thesis focuses on information extraction from spoken documents, extracting two important information: key terms and summaries. It proposes a set of approaches to automatically extract key terms from spoken course lectures including audio signals, ASR transcriptions and slides. We divide the key terms into two types: key phrases and keywords and develop different approaches to extract them in order. We extract key phrases using right/left branching entropy and extract keywords by learning from three sets of features: prosodic features, lexical features and semantic features from Probabilistic Latent Semantic Analysis (PLSA). The learning approaches include an unsupervised method (K-means exemplar) and two supervised ones (AdaBoost and neural network). Very encouraging preliminary results were obtained with a corpus of course lectures, and it is found that all approaches and all sets of features proposed here are useful. With key terms, this thesis proposes an improved approach for spoken lecture summarization, in which random walk is performed on a graph constructed with automatically extracted key terms and PLSA. Each sentence of the document is represented as a node of the graph and the edge between two nodes is weighted by the topical similarity between the two sentences. The basic idea is that sentences topically similar to more important sentences should be more important. In this way all sentences in the document can be jointly considered more globally rather than individually. Experimental results showed significant improvement in terms of ROUGE evaluation.