Extractive Summarization: Limits, Compression, Generalized Model and Heuristics
This work addresses the challenge of text summarization for researchers by providing theoretical limits and a unifying model, though it is incremental in nature.
The paper proves empirical limits on recall and F1-scores for extractive summarizers on DUC datasets using ROUGE evaluation, and introduces a generalized model that integrates dimensions like abstractive vs. extractive and single vs. multi-document summarization.
Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the concept of compressibility of a document and present a new model of summarization, which generalizes existing models in the literature and integrates several dimensions of the summarization, viz., abstractive versus extractive, single versus multi-document, and syntactic versus semantic. Finally, we examine some new and existing single-document summarization algorithms in a single framework and compare with state of the art summarizers on DUC data.