Table 6: Comparison of the decoder information sharing methods and encoder sharing methods for the job advertisement dataset. The metrics are the same as in Table 3. The proposed method (adopting HCL) achieved the best scores (bold) compared to the other sharing methods.

Headline Generation || R-1

Headline Generation || R-2

Headline Generation || R-L

Key Phrase Generation || R-1

Key Phrase Generation || R-2

Key Phrase Generation || R-L

Classification || Accuracy

Baseline (Pointer-Generator Network)

25.1

5.3

21.1

30.9

10.6

28.7

62.8

Proposed (MTL + SD + HCL)

26.9

6.1

22.4

32.8

11.2

30.5

64.4

Comparison of Decoder Information Sharing Method MTL + SD

26.3

6

21.8

32.3

10.4

29.9

63.9

Comparison of Decoder Information Sharing Method MTL + SD + Cascade Model

26.3

5.6

21.6

31.8

10.6

29.5

64.4

Comparison of Decoder Information Sharing Method MTL + SD + Cascade Model (Gold)

26.5

5.8

21.9

32.8

10.4

30.3

64.5

Comparison of Decoder Information Sharing Method MTL + SD + Soft-Parameter Sharing

25.8

5.9

21.4

32.1

10

29.6

64

Comparison of Decoder Information Sharing Method MTL + SD + Non-Hierarchical Consistency Loss

25.9

6

21.4

32.6

10.9

30.2

64

Comparison of Decoder Information Sharing Method MTL + SD + HCL with Normalized Attention Weights

26.2

6

21.7

31.9

10.5

29.5

63.9

Comparison of Encoder Information Sharing Method HCL (SD and MTL are not applied)

25.8

5.6

21.2

31

10.1

28.7

63.1

Comparison of Encoder Information Sharing Method SD + HCL (MTL is not applied)

25.6

5.6

21.5

31.2

10.2

28.9

62.6

It can be observed from Table 6 that the cascade model achieves a lower score than MTL + SD. However, the cascade model (gold) improves the ROUGE-L score. This result indicates that the classification error propagates to the generators when the cascade model is applied. Our proposed HCL has an advantage in that it does not suffer from an error of the classifier, and thus this method achieves the best score. Furthermore, we compare our proposed HCL model with other settings. A soft-parameter sharing method (Guo et al., 2018) penalizes the difference between parameters in pairs of decoders. The non-hierarchical consistency loss is almost the same as the hierarchical consistency loss (Eqn. 10). We replace a ramp function in Eqn. 10 with an absolute value function. Neither the soft-parameter sharing method nor the non-hierarchical consistency loss has the ability to consider the hierarchy among tasks. It can be observed from Table 6 that both methods achieved a lower score than the hierarchical consistency loss. This is because our hierarchical consistency loss enables the model to penalize a multi-task model with hierarchy among the tasks. Although the HCL with normalized-attention weights adopts the hierarchical consistency loss model indicated in Eqn. 10, however, we substitute the normalized attention weights ?ds ij for non-normalized attention weights eds ij . HCL with normalized attention weights is not as effective as HCL with non-normalized attention weights. Our HCL is based on the assumption that if one specific input word is important for both the shorter and longer text generation tasks, the attention weights of the words for the shorter text generation task would be smaller than the attention weights for the longer text generation task. However, the distributions of the normalized attention weights converge to a few words for the shorter text generation task; thus the normalized attention weights does not satisfy the assumption. As a result, our proposed HCL with non-normalized attention weights can accurately compute this inconsistency, contrary to the HCL with normalized attention weights. Comparison of encoder information sharing methods. To determine the dependence of HCL on MTL, we conduct two experiments. HCL applies the hierarchical consistency loss without MTL and SD, while SD + HCL applies the scheduling sampling and hierarchical consistency loss; however, multi-task learning is not applied. Table 6 presents the results of two methods for the job advertisement dataset.