Artificial Intelligence

   

Multi-Document Text Summarization

Authors: Luis Perez, Kevin Eskici

We tackle the problem of multi-document extractive summarization by implementing two well-known algorithms for single-text summarization -- {\sc TextRank} and {\sc Grasshopper}. We use ROUGE-1 and ROUGE-2 precision scores with the DUC 2004 Task 2 data set to measure the performance of these two algorithms, with optimized parameters as described in their respective papers ($\alpha =0.25$ and $\lambda=0.5$ for Grasshopper and $d=0.85$ for TextRank). We compare these modified algorithms to common baselines as well as non-naive, novel baselines and we present the resulting ROUGE-1 and ROUGE-2 recall scores. Subsequently, we implement two novel algorithms as extensions of {\sc GrassHopper} and {\sc TextRank}, each termed {\sc ModifiedGrassHopper} and {\sc ModifiedTextRank}. The modified algorithms intuitively attempt to ``maximize'' diversity across the summary. We present the resulting ROUGE scores. We expect that with further optimizations, this unsupervised approach to extractive text summarization will prove useful in practice.

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[v1] 2017-12-16 00:38:28

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