The C reative S umm 2022 Shared Task: A Two-Stage Summarization Model using Scene Attributes (2022)

Abstract: In this paper, we describe our work for the CreativeSumm 2022 Shared Task, Automatic Summarization for Creative Writing. The task is to summarize movie scripts, which is challenging due to their long length and complex format. To tackle this problem, we present a two-stage summarization approach using both the abstractive and an extractive summarization methods. In addition, we preprocess the script to enhance summarization performance. The results of our experiment demonstrate that the presented approach outperforms baseline models in terms of standard summarization evaluation metrics.

## introduction summarization is an important task in natural language processing research area. although many works have been conducted on summarization, little has been researched on summarizing movie scripts. it is challenging to generate a summary from movie scripts for several reasons. first, movie scripts are long. according to an analysis on hollywood screenplays @xcite , a typical script has an average page count of 110, and can even reach a few hundred pages. the long input length causes disparities when aligning its plot summary with corresponding parts in the script @xcite . summarizing long and multi-faceted document is a classical challenge. as the document gets longer, the computational complexity of summarizing it increases dramatically (gidiotis and tsoumakas, 2020). attempts have been made to address the problem of long document summarization, utilizing discourse in the document @xcite and hierarchical structures to effectively understand long sentences @xcite . @xcite recognizes the importance of discourse in long document summarization. they develop a discourse-aware decoder to capture important points from different discourse sections. @xcite presents a two-stage strategy. they select important sentences in a document using an extractive summarization model, and then summarize them again using a transformer decoder. figure 1 : a sample illustration of our two-stage summarization approach with the movie script of the film 'top gun'. we first create a scene summary from the script using an abstractive summarization model. we then select important sentences using an extractive summarization model. moreover, movie scripts have a complex format, consisting of different components such as dialogues, action directions, scene description, cut transitions, film editing instructions, etc @xcite . movie transcripts are similar to tv scripts, in that they contain dialogues as well as action and filming directions and editing descriptions. various studies have been conducted to extract summaries from tv scripts @xcite . @xcite constructs a dialogue summarization model trained with tv transcripts datasets by crawling them from the websites such as forever dreaming and tvmegasite dataset @xcite . although movie scripts tend to be longer than tv scripts, we can refer to previous studies in tv script summarization to deal with the complex format. summarization methods can be divided into two types: abstractive and extractive summarization. when a document is given as a source text, an abstractive summarization generates a summary that contains text that are not in the source text, whereas extractive summarization re-uses the words in the source text. specifically, the task of extractive summarization is choosing salient words and sentences. the goal of our work is to summarize movie scripts, which is one of the shared task of creative-summ 2022, the automatic summarization for creative writing workshop, at coling 2022. this paper describes a two-stage summarization approach employing both the abstractive and extractive summarization methods to summarize movie scripts. figure 1 illustrates our framework. first, our preprocessing stage merges similar scenes based on character information to enhance the summarization performance. then, the abstractive summarization method produces a summary for each scene-based unit of the script. in particular, we use the dialoglm model @xcite since dialogues in the script are essential for understanding the story. finally, these scene summaries are summarized again by selecting salient scenes via topic modeling and the extractive summarization method. this paper is structured as follows. section 2 describes our method in detail. section 3 reports the results of the experiment. finally, we end with conclusion. ## method our approach is composed of four steps: scene segmentation, dialogue abstraction, salient scene selection, and extractive summarization. figure 2 briefly illustrate our method. ## conclusion in this paper, we present a two-stage approach for the shared task of creative-summ 2022. we first segment the script into scenes and create their summaries using an abstractive summarization model. second, we apply topic modeling to eliminate less important scenes. then, a bert-based extractive summarization model generates the final summary of the movie. the result of evaluation indicates that our approach outperforms baseline models in several metrics. we got 0.4144 for rouge-1, 0.0823 for rouge-2, and 0.3963 for rouge-3 which are better than baseline models. we also got better bertscore such as 0.5163 for precision, 0.5233 for recall and 0.5194 for f1 score.