Input text was truncated to 2560 words. That's about 41.76% of the submission.
2023-05-01 07:10:05,366 - INFO - summarize - Loaded model pszemraj/led-large-book-summary to cpu 2023-05-01 07:10:05,366 - INFO - summarize - Compiling model with Torch 2.0 2023-05-01 07:10:05,368 - INFO - summarize - input parameters: {'do_sample': False, 'early_stopping': True, 'encoder_no_repeat_ngram_size': 4, 'length_penalty': 0.7, 'max_length': 256, 'min_length': 4, 'no_repeat_ngram_size': 3, 'num_beams': 4, 'repetition_penalty': 3.5} 2023-05-01 07:10:05,368 - INFO - summarize - batch_length: 1024, batch_stride: 16 This paper proposes a new approach to learning character types in film. In order to do so, the authors use a combination of traditional character modeling and machine learning techniques to predict behavior of actors based on their actions. They first define a set of roles that are assigned to three different personas, then they fill out each role with specific attributes, such as "female, 28-year old, clumsy," and finally they sketch out the events that will take place in these roles. The goal of the paper is to predict behaviors observed by machines in order to make informed decisions about which characters to assign roles to and how. The authors believe that this approach can be used to predict the behavior of individual actors as well as the overall plot of a given movie. Score: -3.098 This paper aims to explore the power of machine learning in relation to character and actor types in order to predict which actors are likely to be good matchmakers for certain roles in a given movie. To date, we have analyzed 42,306 English-language movies with its release date, country, language, and genre from Freebase as well as information about the actors who play these roles and the nature of the plot. Using a combination of these sources, we attempt to predict the roles of actors based on their roles in the movie. Our work thus far has only been limited in its usefulness as a source of information, but we extend it to include analysis of over 1,000 characters per movie. The authors note that they have used a modified version of the Stanford Core NLP Toolkit to help them analyze this data. Next, we perform a comparison between their own dataset of Freebase and a dataset drawn from Wikipedia. This provides us with both a starting point for understanding Freebase's capabilities and an end point for evaluating our own work. Score: -3.7424 Next, the authors examine two different approaches to analyzing movie data to determine how well our artificial intelligence is adapted to machine learning. The first approach uses word-learning to predict which characters in a movie will be most likely to succeed and which ones will fail. The second approach uses an analysis of latent word datasets to predict on-screen behavior. The word datasets are convolutions of thousands of words with a weighted average of their meanings depending on the class of the word. The goal of this approach is to predict behaviors that are immediately apparent in the text, i.e., actions that have a high degree of ambiguity. For example, if a task is too difficult for one person, another person may be assigned the task. This can be done by training the system on a set of word clusters and then comparing it to a list of real-world scenarios where the decisions made by a single person are extremely important. The more complex the scenario, the greater the importance of knowing when to cut to the good guy vs. the bad guy. In this case, the poor guy might get to choose between eating, drinking, and devouring, but the evil villain gets to choose only eat, drink, and consume. Score: -3.7411 Next, we examine the assumptions made by Imogen about which characters will and will not take on particular roles in the new novel. We start by analyzing the dataset with a simple step-by-step analysis of the tens of thousands of words associated with each character type. Next, we perform a batch of experiments to determine which characters are most likely to assume the role of hero or heroine in the novel. The results of these experiments are presented in Table 2. Score: -2.7449 0%| | 0/4 [00:00Input text was truncated to 2560 words. That's about 41.76% of the submission.
2023-05-01 07:12:20,353 - INFO - summarize - Loaded model pszemraj/led-large-book-summary-continued to cpu 2023-05-01 07:12:20,353 - INFO - summarize - Compiling model with Torch 2.0 2023-05-01 07:12:20,354 - INFO - summarize - input parameters: {'do_sample': False, 'early_stopping': True, 'encoder_no_repeat_ngram_size': 4, 'length_penalty': 0.7, 'max_length': 256, 'min_length': 4, 'no_repeat_ngram_size': 3, 'num_beams': 4, 'repetition_penalty': 3.5} 2023-05-01 07:12:20,354 - INFO - summarize - batch_length: 1024, batch_stride: 16 This paper proposes a new approach to learning character types in film. In order to do this, the authors propose a combination of traditional character-centric approaches to character analysis and machine learning. They examine two different approaches proposed by previous authors, one that treats a persona as an entire class of people and the other that consists of only a set of individuals. The authors believe their approach will be successful because it allows them to study a wide range of possible personas without ever missing any of the usual archetypes. Score: -2.4032 The authors gather information about the 42,306 English-language movies that have been released to date from Freebase in order to predict which actors and their roles in the films. They use a combination of metadata from the film and Freebase to predict the roles of actors based on what they type as "agent verbs, patient verbs, and attribute types". The authors note that this is the first study ever to attempt to learn an actor's role in a single movie. To help with this task, they perform several experiments using both Freebase and Wikipedia as sources of data. Their dataset consists of only those phrases with at least three events per movie and only those sections of the movie where there are more than three events. For example, if a scene occurs in a movie where up to six characters appear at once, our model predicts which actors will appear and which ones will stay only one scene at a time. Additionally, we analyze the gender and age distribution of actors by looking at only those actors whose gender is known. For instance, we see that 66% of all male actors and 66 % of all female actors are male. The authors hypothesize that this gender imbalance may result from either a bias in the portrayal of women in films or a bias towards Free Score: -4.0545 Next, the authors focus on analyzing two different types of data, namely, the words that are used in movies and the phrases that appear within them. In order to understand these terms, we need to understand what a persona is--that is, a set of words associated with a particular class of characters in a movie. A persona can be divided into three parts: 1) the agent who primarily eats and kills actions; 2) the patient who primarily commits killing actions; and 3) the object of the killing actions. For example, a "ZOMBIE" persona might be characterized as an agent whose job it is to eat and kill animals. Now, for the purposes of this paper, we're going to focus on the role that each of these three personas plays in the movie. To help visualize the roles and their relation to one another, we'll use a toy example. ZOM is the ZOM because he's the agent of eating and killing. Kill is the mode of attack for those of us who like to eat; eat is for those who want to drink and eat, and devour is for folks who like...well, you get the picture. Next, we look at two more simple models to help illustrate the role- Score: -3.8109 The temporal analysis of this dataset is performed using a technique called "splitting", which means we only sample words that have multiple occurrences in a given document. This technique allows us to analyze thousands of words at a time, and we collect information about them over a course of thousands of iterations. For each character, we sample them according to their role in the plot. We then use a distributed Dirichlet to calculate the likelihood that each individual character will assume a persona specific to that role. The more you look at a textured list of words, the more likely it is that a particular persona will appear in a particular scene or animation. Score: -2.8778 0%| | 0/4 [00:00