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A Performance of HeterTLS on CovidTLS
Metrics	CR1-F	CR2-F	AR1-F	AR2-F	Date-F
Full Oracle	0.471	0.199	0.388	0.192	0.968
CHIEU (2004)	0.203	0.021	0.008	0.001	0.176
TRAN (2013)	0.218	0.028	0.012	0.001	0.675
MARTS. (2018)	0.249	0.036	0.028	0.001	0.685
DATEWISE (2020)	0.318	0.038	0.036	0.005	0.697
DASG (2021)	0.224	0.030	0.014	0.001	0.621
SDF (2021)	0.439	0.076	0.062	0.011	0.689
HTLS-HAN	0.402	0.062	0.052	0.009	0.656
HTLS-Joint	0.388	0.058	0.048	0.006	0.648
HTLS+Pre-trained	0.447†	0.078†	0.068†	0.012†	0.722†
HeterTLS	0.430	0.072	0.060	0.011	0.704
Table 5: Concatenation- and alignment-based ROUGE-1/2 F1-score for CovidTLS dataset. Best results among model-generated timelines are marked in bold. Symbol  $\dagger$  indicates that our results significantly surpass all baselines using bootstrap test (Dror et al., 2018) with  $p < 0.005$ .
The newly released CovidTLS dataset  ${}^{1}$  describes the outbreak and evolution of the Covid-19 pandemic since the beginning of 2020. Because it is undoubtedly one of the most important worldwide events and affects all aspects of people's lives and work, it has been reported by an unprecedented amount and variety of news articles. The whole corpus was crawled from well-known English journals, while it is annotated with a ground-truth timeline retrieved from a public, authoritative website.
Table 5 shows the excellent performance of HeterTLS on this new dataset. Since CovidTLS is a large-scale dataset containing 26,376 documents and 791,280 sentences per topic, the pretrained node representations can escape from the local optimal solution through massive iteration processes and converge to its globally optimal solution. We are convinced that the lightweight HeterTLS is more effective for small-scale datasets while HeterTLS initialized using pre-trained language models attains better results on large-scale datasets.
B Impact of parameters on T17, Crisis, and CovidTLS Datasets
We selected several sets of  $\lambda$  and  $\delta$  to test the performance of HeterTLS and give a general overview in Figure 6 as in Sec. 5.4. HeterTLS performed the
best when  $\lambda = 1.5$  and  $\delta = 0.6$  on T17 dataset, while  $\lambda \in [2.0,2.5]$  and  $\delta \in [0.5,0.55]$  on Crisis and CovidTLS datasets. Even though a larger  $\delta$  coupled with a smaller  $\lambda$  works better, HeterTLS seldom failed to converge as the parameters changed. Therefore, we reasonably believe that our proposed model is not sensitive to parameters.
C Attach date labels for sentences
Since it is a difficult problem to correctly extract the chronological order of events from time stamped-free texts, we therefore attempt to only attach dates to the sentences extracted from news articles. We assume that the first date expression detected in a sentence  $s$  is the date of the event mentioned in  $s$ . We further craft simple rules to detect date expressions in sentences and resolve them to absolute dates using the date of the article as a reference. For example, with "today" parsed as the publication date of the article, "September" and "Sunday" indicate the last September and Sunday before the article date. In the case that no date expression is detected in the entire sentences  $s$ ,  $date(s)$  is taken to be the publication date of the article containing  $s$ . Although this assumption is frequently incorrect in document types such as biographies, literary writings, or historical texts, we find it is reasonable for news articles. News, by definition, reports up-to-date events.
D Edge Initialization
We give a more detailed description of the edge initialization in the main body. To leverage the saliency of each word in different sentences and dates, we propose using term frequency-inverse sentence frequency (TF-ISF) and term frequency-inverse date frequency (TF-IDATEF) weights to initialize edges in  $E_{w - s}$  and  $E_{d - w}$ .
$$
T F _ {i, j} = \frac {n _ {i , j}}{\sum_ {k} n _ {k , j}}, \tag {15}
$$
$$
I S F _ {i} = \log \frac {| S |}{| \{j : w _ {i} \in s _ {j} \} |}, \tag {16}
$$
$$
I D A T E F _ {i} = \log \frac {| D |}{| \{j : w _ {i} \in d _ {j} \} |}, \tag {17}
$$
where  $n_{i,j}$  indicates the number of occurrences of word  $w_i$  in sentence  $s_j$  (for edges in  $E_{w-s}$ ) or date  $d_j$  (for edges in  $E_{d-w}$ ), and the denominator of Eq. 15 is the sum of the number of occurrences of all words in  $s_j$  or  $d_j$ . In Eqs. 16 and 17,  $|S|$  and  $|D|$  respectively denote the total number of sentences
$^{1}$ https://github.com/MorenoLaQuatra/SDF-TLS
Figure 6: Impact of parameters on T17, Crisis, and CovidTLS datasets
and dates in the corpus, and  $|\{j:w_i\in s_j\} |$  and  $|\{j:w_i\in d_j\} |$  are the numbers of sentences or dates where term  $w_{i}$  appears.
Intuitively, some words, e.g., articles such as "the" and "a", appear in many sentences and dates, while other words, e.g., "Harry Potter", are not so frequent. Therefore, words with lower ISF/IDATEF values are not so important and usually have no specific meaning. Conversely, words with higher ISF/IDATEF values might be important and indicate salient information or the topic of the article. This assumption allows HeterTLS to distinguish key points from non-key points.