Huang et al., 2020) present SportsSum, the first large-scale sports game summarization dataset with 5,428 samples. The New England Patriots had a spectacular comeback defeating the Atlanta Falcons in a historical game. In this paper, we study the sports game summarization on the basis of SportsSum. The comprehensive database of scoring events we use here to investigate such questions is unusual for both its scope (every league game over 9-10 seasons), its breadth (covering four sports), and its depth (timing and Dallas Cowboys Men’s attribution information on every point in every game). Then no Nations League group winner ranked lower than these four teams-such as Albania-has any chance to go to the play-offs in the alternative way, hence tanking makes no sense. The two virtual images were then smoothly blended. Aliasing was simulated by first downscaling each video, then upscaling it back to its original dimensions. It is straightforward to convert a time-stamp back to a channel number for further investigation and analysis. The main idea of the role representation is that each player is not distinguished by an uniform number but by a relative position (role) assigned them at each frame of the data. In the columns KC1 and KC4 we report number of noisy examples which are rejected, as we sequentially apply each knowledge constraint.
We resize the whole dataset into 720P. In total, our released version contains 18422 training examples from 1574 clips and 6577 validation instances from 555 clips. The dataset was divided into training sets, test sets and validation sets. Meanwhile, improving training methods by reformulating a ML problem can also have a significant effect on accuracy, which nearly 40% of the studies intend to explore. Abs-PGNet (See et al., 2017) and Abs-LSTM (abstractive summarization models) achieve better performance since they can alleviate different styles issue, but still have the drawback of dealing with long texts. TextRank (Mihalcea and Tarau, 2004) and PacSum (Zheng and Lapata, 2019) are extractive summarization models, which are limited by the different text styles between commentaries and news. According to our observations on SportsSum, more than 15% of samples have noisy sentences in the news articles due to its simple rule-based data cleaning process; (2) The pseudo-labeling algorithm used in existing two-step models only considers the semantic similarities between news sentences and commentary sentences but neglects the lexical overlap between them, which actually is an useful clue for generating the pseudo label; (3) The existing approaches rely on direct stitching to constitute news with the (rewritten) selected sentences, resulting in low fluency and high redundancy problems due to each (rewritten) selected sentence is transparent to other sentences.
So, we propose a variant MMR algorithm by incorporating the fluency of each rewritten sentence. R denotes the selected news sentence set. In order to provide training data for both selector and rewriter of the two-step model, a pseudo-labeling algorithm is introduced to find for each news sentence a corresponding commentary sentence according to their timeline information as well as semantic similarities. Here, a RoBERTa (Liu et al., 2019) is employed to extract the contextual representation of commentary sentences. Secondly, lexical overlaps between news sentences and commentary sentences are taken into account by our advanced pseudo-labeling algorithm. Selector. Different from existing two-step model (Huang et al., 2020), which purely use TextCNN (Kim, 2014) as selector and ignores the contexts of a commentary sentence, we design a context-aware selector which can capture the semantics well with a sliding window. Component 4: Post detection of clock bounding boxes, we scale the bounding box coordinates to original image resolution in the videos so that we can crop out natural sized clock images (again using ffmpeg decode). Post decode, we hold on to the cropped clock image objects (in memory) for every frame (at the reduced frame rate of processing) and send them to the text detection model to predict the text bounding boxes within the clock (as in Figure 5(a) component 4). Component 5: The text regions identified within a clock are then passed to the text recognition model to extract textual strings.
Parse video (which is largely ffmpeg decode, running on cpus) time differently as they have different cpus. Advertisements and irrelevant hyperlink text: We find that about 9.8% (531/5428) of news articles in SportsSum have such noise. Our proposal would have made the draw of the European Qualifiers to the 2022 FIFA World Cup fairer. The six confederations of FIFA organise parallel tournaments. The group winners qualify for the 2022 FIFA World Cup. The authors concluded that traditional statistics outperformed the newer performance metrics in predicting outcomes of single games using 10-fold CV, while the ANN displayed the best accuracy results of 59%. Research in extracting more informative features and predicting winners of the NHL playoffs was cited as future work as well as incorporating knowledge from similar sports such as soccer. Finally, we consider centralized training of the two home agents where a single policy controls them at the same time. Prohibited team clashes: The UEFA Executive Committee decided that six pairs of teams cannot be drawn into the same group.333 Among the six, two prohibited clashes turned out to be avoidable due to the other constraints of the draw. Aspect ratio and size sensitive custom data augmentation (represented as custom data augmentation in algorithm 1 step 11): Post correction/filtering of the text string and bounding box labels using knowledge constraints mentioned in section 3.1, we calculate the possible range of height and width across different sports clocks (NBA/soccer/NFL/NHL).