# Similarity Calculation via Passage-Level Event Connection Graph
## 5 Experiments and Analyses
### Experimental Results
#### 5.2.3 Case Study  
In our similarity calculation, we need to extract some nodes from the event connection graph to represent the core event mentioned in the text. In the following table, we just show the extracted nodes (i.e., triggers and arguments) from some sampled texts. We sample 6 texts from our English testing corpora, three for long texts (noted as A1, A2, A3) and three for short texts (noted as S1, S2, S3). The short texts are news caption. The contents of the chosen texts can be found in (A1: [[https://www.bbc.com/news/technology-52391759](https://www.bbc.com/news/technology-52391759)]([https://www.bbc.com/news/technology-52391759](https://www.bbc.com/news/technology-52391759)); A2: [[http://news.bbc.co.uk/sport2/hi/football/europe/8591081.stm](http://news.bbc.co.uk/sport2/hi/football/europe/8591081.stm)]([http://news.bbc.co.uk/sport2/hi/football/europe/8591081.stm](http://news.bbc.co.uk/sport2/hi/football/europe/8591081.stm)); A3: [[https://www.bbc.com/news/business-52467965](https://www.bbc.com/news/business-52467965)]([https://www.bbc.com/news/business-52467965](https://www.bbc.com/news/business-52467965)); S1: [[https://www.bbc.com/news/uk-51259479](https://www.bbc.com/news/uk-51259479)]([https://www.bbc.com/news/uk-51259479](https://www.bbc.com/news/uk-51259479)); S2: [[https://www.bbc.com/news/business-44789823](https://www.bbc.com/news/business-44789823)]([https://www.bbc.com/news/business-44789823](https://www.bbc.com/news/business-44789823)); S3: [[https://www.bbc.com/news/business-52466864](https://www.bbc.com/news/business-52466864)]([https://www.bbc.com/news/business-52466864](https://www.bbc.com/news/business-52466864)) (accessed on 17 May 2022). These texts can be accessed till the pages are deleted). Since keywords can also be treated as the content representation of the text, we also show the keyword extraction results (the top five keywords) obtained by LDA and TextRank, two popular unsupervised keyword extraction algorithms.  
As shown in Table 3, among the chosen words, some are the same across different extraction methods while some are distinct. Taking the contents of the sampled texts into  
Figure 6: The histogram to see the results obtained by different event extraction methods (blue color indicates text similarity calculation task and orange color indicates event extraction task).  
consideration, the words extracted by our calculation can exactly cover the core event mentioned in the sampled texts, though the number of extracted words is often less the number from the other two keyword extraction methods. On the contrary, the words extracted by TextRank and LDA are not always related to the core event. This situation is obvious for long texts. For example, for A1, its core event is \"Apple iPhone has a software leak on email app\". The keywords extracted from this text via TextRank and LDA both include \"ZecOps\". This word repeats many times in A1. Thus, it is chosen as keyword, whereas this word indicates the source where the news is published. It is not the part of the core event. The reason to this situation is that traditional keyword extraction methods often take the shallow statistics, such as frequency or distribution, to measure word importance. Such an approach causes \"ZecOps\" to be incorrectly chosen. In our calculation, when measuring the importance of one word, we consider the effect of the event which includes this word. In detail, only if the event is emphasized by one text, the word included by this event can be treated as the representation of this text. In the text of long length, there may mention several events. The frequently occurring words may not be included by the core event, such as the words \"ZecOps\" and \"Rooney\" included by A1 and A2. Thus, they may be extracted incorrectly. Regarding the texts of short length, they only include one or two sentences. A few events are mentioned. The frequently occurring words are mostly included by the core event. Thus, among the short texts, the words extracted by the three methods are almost same.