Shasha Xie

Using corpus and knowledge-based similarity measure in Maximum Marginal Relevance for meeting summarization

MMR (maximum marginal relevance) is widely used in summarization for its simplicity and efficacy, and has been demonstrated to achieve comparable performance to other approaches for meeting summarization. How to appropriately represent the similarity of two text segments is crucial in MMR. In this paper, we evaluate different similarity measures in the MMR framework for meeting summarization on the ICSI meeting corpus. We introduce a corpus- based measure to capture the similarity at the semantic level, and compare this method with cosine similarity and centroid score that only considers the salient words in the segments. Our experimental results evaluated by the ROUGE summarization metrics show that both the centroid score and the corpus-based similarity measure yield better performance than the commonly used cosine similarity. In addition, adding part-of-speech information in the corpus-based approach helps for the human transcripts condition, but not when using ASR output.

Graph-based submodular selection for extractive summarization

We propose a novel approach for unsupervised extractive summarization. Our approach builds a semantic graph for the document to be summarized. Summary extraction is then formulated as optimizing submodular functions defined on the semantic graph. The optimization is theoretically guaranteed to be near-optimal under the framework of submodularity. Extensive experiments on the ICSI meeting summarization task on both human transcripts and automatic speech recognition (ASR) outputs show that the graph-based submodular selection approach consistently outperforms the maximum marginal relevance (MMR) approach, a concept-based approach using integer linear programming (ILP), and a recursive graph-based ranking algorithm using Googles PageRank.

Improving supervised learning for meeting summarization using sampling and regression

Meeting summarization provides a concise and informative summary for the lengthy meetings and is an effective tool for efficient information access. In this paper, we focus on extractive summarization, where salient sentences are selected from the meeting transcripts to form a summary. We adopt a supervised learning approach for this task and use a classifier to determine whether to select a sentence in the summary based on a rich set of features. We address two important problems associated with this supervised classification approach. First we propose different sampling methods to deal with the imbalanced data problem for this task where the summary sentences are the minority class. Second, in order to account for human disagreement for summary annotation, we reframe the extractive summarization task using a regression scheme instead of binary classification. We evaluate our approaches using the ICSI meeting corpus on both the human transcripts and speech recognition output, and show performance improvement using different sampling methods and regression model.

Integrating prosodic features in extractive meeting summarization

Speech contains additional information than text that can be valuable for automatic speech summarization. In this paper, we evaluate how to effectively use acoustic/prosodic features for extractive meeting summarization, and how to integrate prosodic features with lexical and structural information for further improvement. To properly represent prosodic features, we propose different normalization methods based on speaker, topic, or local context information. Our experimental results show that using only the prosodic features we achieve better performance than using the non-prosodic information on both the human transcripts and recognition output. In addition, a decision-level combination of the prosodic and non-prosodic features yields further gain, outperforming the individual models.

Evaluating the effectiveness of features and sampling in extractive meeting summarization

Feature-based approaches are widely used in the task of extractive meeting summarization. In this paper, we analyze and evaluate the effectiveness of different types of features using forward feature selection in an SVM classifier. In addition to features used in prior studies, we introduce topic related features and demonstrate that these features are helpful for meeting summarization. We also propose a new way to resample the sentences based on their salience scores for model training and testing. The experimental results on both the human transcripts and recognition output, evaluated by the ROUGE summarization metrics, show that feature selection and data resampling help improve the system performance.

Using n-best recognition output for extractive summarization and keyword extraction in meeting speech

There has been increasing interest recently in meeting understanding, such as summarization, browsing, action item detection, and topic segmentation. However, there is very limited effort on using rich recognition output (e.g., recognition confidence measure or more recognition candidates) for these downstream tasks. This paper presents an initial study using n-best recognition hypotheses for two tasks, extractive summarization and keyword extraction. We extend the approach used on 1-best output to n-best hypotheses: MMR (maximum marginal relevance) for summarization and TFIDF (term frequency, inverse document frequency) weighting for keyword extraction. Our experiments on the ICSI meeting corpus demonstrate promising improvement using n-best hypotheses over 1-best output. These results suggest worthy future studies using n-best or lattices as the interface between speech recognition and downstream tasks.

Impact of automatic sentence segmentation on meeting summarization

This paper investigates the impact of automatic sentence segmentation on speech summarization using the ICSI meeting corpus. We use a hidden Markov model (HMM) for sentence segmentation that integrates the N-gram language model and pause information, and a maximum marginal relevance (MMR) based extractive summarization method. The system-generated summaries are compared to multiple human summaries using the ROUGE scores. The decision thresholds from the segmentation system are varied to examine the impact of different segments on summarization. We find that (1) using system generated utterance segments degrades summarization performance compared to using human annotated sentences; (2) segmentation needs to be optimized for summarization instead of the segmentation task itself, however, the patterns are slightly different from prior work for other tasks such as parsing; and (3) there are effects from different summarization evaluation metrics as well as speech recognition errors.

Using N-Best Lists and Confusion Networks for Meeting Summarization

The incorrect speech recognition results usually have a negative impact on the speech summarization task, especially on the meeting domain where the word error rate is often higher than other speech genres. In this paper we investigate using rich speech recognition results to improve meeting summarization performance. Two kinds of structures are considered, n-best hypotheses and confusion networks. We develop methods to utilize multiple word and sentence candidates and their recognition confidence for summarization under an unsupervised framework. Our experimental results on the ICSI meeting corpus show that our proposed method can significantly improve summarization performance over using 1-best recognition output, evaluated by both ROUGE-1 and ROUGE-2 scores. We also find that if the task is to generate speech summaries or identify salient segments, using rich speech recognition output is just as effective as using human transcripts. In addition, we discuss the difference between n-best lists and confusion networks, and analyze the word error rate in the exacted summary sentences.