SlowGuess/NAACL_2022_OCR huggingface dataset:
When fine-tuning on the RST Annotated Dataset, we used an effective batch size of 44 and trained using an Early Stopping rule allowing for at most one epoch to pass with no improvement. The maximum target sequence length is 270 tokens. The maximum RST Tree Size is 36 parent nodes. The maximum Key Phrase sequence size is 64 tokens. These models take approximately 5 epochs to converge which takes approximately 10 hours.
D Keyphrase Extraction using TextRank
Our second conditioning factor is the phrases that are important to the generated text. This importance is determined by the steps listed below:
Noun Chunks and Named Entities Given a text  $x$ , the noun chunks and named entities are extracted to form a set of sub-phrases  $[x_1^{sp}, x_2^{sp}, \ldots, x_N^{sp}] \in x$ .
Graph Formation Separately, a graph  $G$  is formed from text  $x$  by extracting all words  $w_{i}$  that have a Part-Of-Speech tag of either 'Adjective', 'Noun', 'Proper Noun' or 'VERB'. These words are lemmatised and form nodes  $V_{i} \in G$ .
Edge Creation A weighted edge  $w_{ij}$  between nodes  $V_{i}, V_{j}, i \neq j$  has a weight of 1 if the distance between the words corresponding to  $V_{i}, V_{j}$  is less than some threshold  $k$ . In other TextRank implementations factors such as word length, position and frequency can be used to scale  $w_{ij}$ .
Node Scoring The TextRank score  $\mathrm{S}(\mathrm{V_i})$  of a node  $V_{i}$  is initialised to a default value. Then Page Rank's adapted Eigenvector centrality measure is used to calculate the importance of each node  $V_{i}$ . S  $(\mathrm{V_i})$  is iteratively updated using the equation below, until convergence is reached:
$$
\mathrm {S} _ {V} \left(\mathrm {V} _ {\mathrm {i}}\right) = (1 - \mathrm {d}) + \left(\mathrm {d} \times \sum_ {j \in \mathcal {N} (\mathrm {i})} \frac {w _ {\mathrm {j i}} \times \mathrm {S} \left(\mathrm {V} _ {\mathrm {j}}\right)}{\sum_ {\mathrm {k} \in \mathcal {N} (\mathrm {j})} w _ {\mathrm {j k}}}\right)
$$
where  $d$  is a damping factor and set to 0.85 as in (Mihalcea and Tarau, 2004) and  $\mathcal{N}(\mathrm{i})$  are the set of indices of the neighbours of  $V_{i}$ .
Keyphrase Scoring Given the set of node scores  $\mathrm{S}(\mathrm{V_i})$ , the score of a sub-phrase  $x_{n}^{sp}$ , with  $L$  words, is computed by summing the scores of the words it contains normalised by its length  $+1$  to favor
Relation	Nuclei	Satellite
Attribution	the effect	the factor which it can be attributed
Background	text whose understanding is being facilitated	text whose understanding is being facilitated
Cause	action/situation	factor which resulted in action/situation's occurrence
Comparison	a comparison between two or more subjects/objects	NA
Condition	action/situation resulting from the occurrence of a conditioning situation	conditioning situation
Contrast	one alternate	the other alternative
Elaboration	basic information	additional information
Enablement	an action/event enabled by a factor	the factor
Evaluation	a situation	an evaluative comment about the situation
Explanation	a statement	the supporting statement to explain the statement
Joint	a list or dis-junction	NA
Manner-Means	the action (being) performed	the manner or means by which the action was performed/achieved
Topic-Comment	a statement such as a question, topic or statement	a paired statement such as an answer / topic-comment or response
Summary	a statement	a restatement, that is shorter
Temporal	a statement with temporal dependence	factor that is depended on
Topic-Change	a shift from this topic A to	a shift to this topic B
Same-Unit	Used to link parts of discourse separated by embedded discourse relation	NA
Textual-Organization	used to link parts of discourse separated by embedded discourse relation	NA
Null	Non-classified	NA
Table A1: The RST relations Schema: A schema providing an interpretation for the RST relations our framework utilises. Some of the descriptions are extracted from (Taboada and Mann, 2006) and (Carlson et al., 2003)
Parent Nodes' Nuclearity	Label	Relationship between sibling does
Nuclei-Nuclei	NN	The left sibling's sub-text is equally as important as the right sibling's sub-text.
Nuclei-Satellite	NS	The left sibling's sub-text is the important part of the parent node's text.
Satellite-Nuclei	SN	The right sibling's sub-text is the important part of the parent node's text.
Null	Null	Non-classified
Table A2: A RST Nuclearity Schema: A schema providing an interpretation of the RST nuclearity our framework uses.
longer  $n$ -grams. This is shown in the equation below:
$$
\mathrm {S _ {k p} (x _ {n} ^ {s p}) = \frac {\sum_ {l = 1} ^ {L} S _ {V} (V _ {l})}{L + 1}}
$$
Keyphrase candidates are then ranked and redundant candidates filtered out. Two candidate phrases are considered redundant if they have the same lemmatised form.
E Argumentation Generation Examples
In this section, we show some example text generated from RSTGen in comparison to baseline models, where text with poor coherence is underlined. We can observe that CTRL generates coherent text but with a shorten length. Both PAIR and DYPLOC exhibit some inconsistency in their generated text. On the contrary, RSTGen generates much longer text with its inconsistency appearing to be more subtle.
CTRL	It is important when you're reading old texts like the Bible to remember that it was made to be analyzed, which the human race has been doing for a couple of hundred years by now. If I were to come up to someone and say "How can you believe in a religion that said all types of animals converged to the Middle East to get on a big boat to be saved?" the person would respond "I have faith. I don't need to question what I have been taught."
PAIR	It is important to remember what reading your texts about your Bible and remember that you've made what you analyzed to human race " but remember about your couple of hundred years ago they were born? I've been taught to think religion, but I've been taught to have types of animals I haven't been in Middle East and I've never been on boat that a saved person to respond to their faith in the US. My question is, do you really?
DYPLOC	I think it's important to read the Bible as a text, because it's important to remember that the Bible was made up and analyzed by the human race for a couple hundred years. The question is, what is it taught to us? How do we know that a person responds to their faith? Religion is the type of animal that converges on a big boat and saves the middle east.
RSTGen	It is a lot more likely to be analyzed by the human race than any other book of text, or even just some couple of important texts that have been written in an attempt to make sense of what we've done for ourselves and our own species. I'm not sure how you can possibly say this without reading all sorts of different perspectives on these topics as opposed to being made up about something like "the world" which has never really been studied at all - it's only ever happened once. The whole point of your argument rests on whether people are actually doing anything wrong with their minds (which isn't always true). It doesn't mean they aren't trying to explain things beyond thinking through them... but rather why there was no reason to believe those questions were asked when someone else said 'why did god exist?' What if one person had already read such answers? If another person hadn't understood science then wouldn't he simply put themselves into question every single-made assumptions?
Table A3: Argument Generation Example Output: We highlight parts of the generated text that correspond to poor coherence. In this example we notice at least one inconsistency in the output for PAIR and DYPLOC. However, the inconsistency in the RSTGen appears to be more subtle.