TGSum: Build Tweet Guided Multi-Document Summarization Dataset
Table 4: ROUGE(%) comparison.
['Method', 'ROUGE-1', 'ROUGE-2']
Now we verify the accuracy of our ILP-based ROUGE upper bound generation algorithm. We choose λ∈0,1,0.0001, which means selecting sentences according to ROUGE-2, ROUGE-1 and their combination. We refer to the corresponding model summaries as UB-1, UB-2 and UB-Combination respectively. As a contrast, we design a baseline applying the greedy algorithm. It iteratively adds the sentence bringing the maximal ROUGE gain into the summary. Likewise, the summaries generated according to ROUGE-1 and ROUGE-2 are called GA-1 and GA-2. Obviously, the ILP solution achieves much larger ROUGE scores than the greedy algorithm. Then we compare three types of summaries derived from different λ values. It is noted that both UB-2 and UB-Combination reach the upper bound of ROUGE-2, but the ROUGE-1 score of UB-Combination is higher. We thereby include the summaries generated by UB-Combination in TGSum as reference summaries.