SlowGuess/NAACL_2021_OCR huggingface dataset:
Model	ROUGE-1	ROUGE-2	ROUGE-L
F	P	R	F	P	R	F	P	R
BART (Lewis et al., 2020)	45.15	49.58	45.97	21.66	23.95	22.16	44.46	48.92	44.26
S-BART w. Discourse α = 0 †	45.40	50.22	45.86	21.96	24.49	22.25	44.56	49.32	44.13
S-BART w. Action α = 0 †	45.47	50.82	45.42	22.23	24.96	22.34	44.55	49.69	43.75
S-BART w. Discourse&Action α = 0 †	45.59	51.47	45.09	22.42	25.51	22.27	44.67	50.24	43.52
S-BART w. Discourse α = 1 †	45.89	51.34	45.87	22.50	25.26	22.33	44.83	49.93	44.17
S-BART w. Action α = 1 †	45.67	50.25	46.44	22.39	24.70	22.96	44.86	49.29	44.75
S-BART w. Discourse&Action α = 1 †	46.07	51.13	46.24	22.60	25.11	22.81	45.00	49.82	44.47
Table 8: Results on SAMSum Corpus. ROUGE-1, ROUGE-2 and ROUGE-L scores for different models on the test set. Results are averaged over three random runs.  $\dagger$  means our methods.
Discourse Type	SAMSum	ADSC
Comment	19.3%	42.7%
Clarification	15.2%	13.3%
Elaboration	2.3%	0.1%
Acknowledgement	8.4%	0.9%
Explanation	2.8%	0.3%
Conditional	0.2%	0%
QA pair	21.5%	12.3%
Alternation	0.3%	0.6%
Result	5.5%	0.2%
Background	0.4%	0%
Narration	0.4%	0%
Correction	0.4%	1.1%
Continuation	0.9%	7.5%
Q-Elab	2.5%	0%
Parallel	0.9%	0%
Contrast	1.0%	19.5%
Table 9: The distribution of predicted discourse relation types on SAMSum Corpus and ADSC Corpus.
(Asher et al., 2016) with default settings  $^{5}$  to get the link prediction and relation classification models to label discourse relations in SAMSum and ADSC corpus. The distribution of the relation types in two datasets were shown in Table 9. The major discourse relations in daily conversations are Comment, Clarification and QA pairs, while the main discourse relations in debate are Comment, Contrast, Clarification and QA pairs.
B Impact of Different ReZero Weight Initializations
We tested our structure-aware BART (S-BART w. Discourse/Action) within two ReZero settings: (i) initializing  $\alpha$  from 0, (ii) initializing  $\alpha$  from 1. And the results were shown in Table 8. S-BART with 1 as the initialized ReZero weight outperformed
that with 0 under under all graph settings, suggesting utilizing more information from graphs would bring in more performance boosts.
$^{5}$ https://github.com/shizhouxing/ DialogueDiscourseParsing