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A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Vanderwende, Lucy Nenkova, Ani Mckeown, Kathleen |
| Description | In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Abstract | The usual approach for automatic summarization is sentence extraction, where key sentences from the input documents are selected based on a suite of features. While word frequency often is used as a feature in summarization, its impact on system performance has not been isolated. In this paper, we study the contribution to summarization of three factors related to frequency: content word frequency, composition functions for estimating sentence importance from word frequency, and adjustment of frequency weights based on context. We carry out our analysis using datasets from the Document Understanding Conferences, studying not only the impact of these features on automatic summarizers, but also their role in human summarization. Our research shows that a frequency based summarizer can achieve performance comparable to that of state-of-the-art systems, but only with a good composition function; context sensitivity improves performance and significantly reduces repetition. |
| File Format | |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Input Document Influence Summarization Usual Approach System Performance Key Sentence State-of-the-art System Sentence Importance Automatic Summarization Sentence Extraction Word Frequency Automatic Summarizers Composition Function Frequency Weight Compositional Context Sensitive Multi-document Summarizer Context Sensitivity Content Word Frequency Good Composition Function Document Understanding Conference Human Summarization |
| Content Type | Text |
| Resource Type | Proceeding |
