SlowGuess/NAACL_2022_OCR huggingface dataset:
2005-2007 for DUC and 2008-2011 for TAC). The source document sentences found in Copeck and Szpakowicz (2005) were made available from 2005-2008. We made use of all the years except 2005, since we found this year to be containing more varied documents within a topic, which yielded noisier automatic alignments between SCU labels and source document sentences.
C Manual Target Re-annotation
Once we removed most of the filtering pipeline of PYRFUS, we noticed that almost  $20\%$  of the fusion input clusters share more than one SCU label. To accommodate, we manually re-label such clusters using all shared SCU labels into a single sentence. For example, for the following two SCU labels: Clinical trials typically involve three phases and Clinical trials involve an average of 200 patients per trial, a new merged fusion label would be: Clinical trials typically involve three phases and an average of 200 patients per trial.
D Examples of Fusion Instances
Table 7 presents 3 examples of fusion instances originating from different datasets. As previously mentioned, DISPARATE fusion involves the fusion of input sentences that often originate from a single document, containing little content overlap but related in discourse. The data used was taken from Lebanonff et al. (2020) and included 1599 samples. PYRFUS and PYRFUS++ contain examples originating from multi-text settings, while PYRFUS contains only inputs from reference summaries, and PYRFUS++ is enhanced with document source sentences, along with more complex examples.
E Training a Fusion Baseline
As described in section 5, we train two sentence fusion baselines using a pre-trained auto-encoder BART base model (Lewis et al., 2020), on PYRFUS and PYRFUS++ respectively. We used the training script  ${}^{9}$  made available by the transformers library (Wolf et al., 2020) with the following parameters: 4 training epochs and a learning rate of 3e-5. A "steps" evaluation parameter was used with 5000 evaluation steps and an evaluation beam of 6. Max source input was limited to 265 while max target length was set to 30. Minimum target length were
$^{9}$ https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/binetune Trainer.py
Filters are not mutually exclusive, therefore there can be an instance that is affected by multiple filters.
Source	Fusion Inputs	Fused Output
DISPARATE	(A) The bodies showed signs of torture.	The bodies of the men, which showed signs of torture, were left on the side of a highway in Chilpencingo.
(B) They were left on the side of a highway in Chilpencingo, about an hour north of the tourist resort of Acapulco in the state of Guerrero.
PYRFUS++	(A) Secret zombie networks, called botnets, infect up to millions of personal computers and countries such as China restrict internet usage.	China uses censorship to fight internet crimes.
(B) China and Iran censor the Internet against subversion or immorality.
PYRFUS	(A) Overseas hackers accessed confidential information from South Korea.	Hackers accessed information from South Korea.
(B) South Korea's presidential mansion came under attack during 2008 from overseas hackers.
Table 7: Examples of different fusion instances from separate sources. PYRFUS and PYRFUS++ contain sentences originating from a multi-text setting, with related events expressed redundantly and differently, motivating information consolidation. The DISPARATE fusion dataset (Lebanoff et al., 2020) uses related sentences originating in the same document, but do not necessarily carry redundancies. Instead, it tries to model the correct discourse structure that can fuse the inputs into one sentence.
set to 4, given our minimum requirements for fusion labels. The final evaluated score reported was an average score over 20 different trained models. This is due to BART being highly sensitive to the ordering of the input sentences. Both baseline models were trained using the train/test splits that were reported in Thadani and McKeown (2013), using DUC years 2005-2007 for test, TAC 2011 for dev, and TAC 2008-2010 for train.