Movie screenplay summarization is challenging, as it requires an understanding of long input contexts and various elements unique to movies. Large language models have shown significant advancements in document summarization, but they often struggle with processing long input contexts. Furthermore, while television transcripts have received attention in recent studies, movie screenplay summarization remains underexplored. To stimulate research in this area, we present a new dataset, MovieSum, for abstractive summarization of movie screenplays. This dataset comprises 2200 movie screenplays accompanied by their Wikipedia plot summaries. We manually formatted the movie screenplays to represent their structural elements. Compared to existing datasets, MovieSum possesses several distinctive features: (1) It includes movie screenplays, which are longer than scripts of TV episodes. (2) It is twice the size of previous movie screenplay datasets. (3) It provides metadata with IMDb IDs to facilitate access to additional external knowledge. We also show the results of recently released large language models applied to summarization on our dataset to provide a detailed baseline.

In the japanese anime industry, predicting whether an upcoming product will be popular is crucial. This paper presents a dataset and methods on predicting anime popularity using a multimodal textimage dataset constructed exclusively from freely available internet sources. The dataset was built following rigorous standards based on real-life investment experiences. A deep neural network architecture leveraging GPT-2 and ResNet-50 to embed the data was employed to investigate the correlation between the multimodal text-image input and a popularity score, discovering relevant strengths and weaknesses in the dataset. To measure the accuracy of the model, mean squared error (MSE) was used, obtaining a best result of 0.011 when considering all inputs and the full version of the deep neural network, compared to the benchmark MSE 0.412 obtained with traditional TF-IDF and PILtotensor vectorizations. This is the first proposal to address such task with multimodal datasets, revealing the substantial benefit of incorporating image information, even when a relatively small model (ResNet-50) was used to embed them.

We study the problem of controlling the difficulty level of text generated by Large Language Models (LLMs) for contexts where end-users are not fully proficient, such as language learners. Using a novel framework, we evaluate the effectiveness of several key approaches for this task, including few-shot prompting, supervised finetuning, and reinforcement learning (RL), utilising both GPT-4 and open source alternatives like LLama2-7B and Mistral-7B. Our findings reveal a large performance gap between GPT-4 and the open source models when using prompt-based strategies. However, we show how to bridge this gap with a careful combination of finetuning and RL alignment.

Stories are a very compelling medium to convey ideas, experiences, social and cultural values. Narrative is a specific manifestation of the story that turns it into knowledge for the audience. In this paper, we propose a machine learning approach to capture the narrative elements in movies by bridging the gap between the low-level data representations and semantic aspects of the visual medium. We present a Memory-Augmented Video Semantic Network, called Video SemNet, to encode the semantic descriptors and learn an embedding for the video. The model employs two main components: (i) a neural semantic learner that learns latent embeddings of semantic descriptors and (ii) a memory module that retains and memorizes specific semantic patterns from the video. We evaluate the video representations obtained from variants of our model on two tasks: (a) genre prediction and (b) IMDB Rating prediction. We demonstrate that our model is able to predict genres and IMDB ratings with a weighted F-1 score of 0.72 and 0.63 respectively. The results are indicative of the representational power of our model and the ability of such representations to measure audience engagement.

For the Hemingway one, please note that the first 3 sentences are me, not GPT-3. In my various tweets, ' ' delimits the prompt (because there's nothing else in the playground UI right now which keeps visual track). Anyway, the interesting thing here is that I spent a good hour trying to get GPT-3 to do this. I knew it could do it based on how it could do Tom Swifties and the Turing dialogue and the other things, but I just couldn't quite figure out how to get it to do so. At one point, I was trying the idea of writing a plot summary and asking it to then "Please write a plot summary of Harry Potter in the style of Ernest Hemingway", and it copied my plot summary verbatim and then added on the sarcastic comment: It still sounds like it came straight from Harry Potter and the Philosopher's Stone.

This paper analyzes challenges in cloze-style reading comprehension on multiparty dialogue and suggests two new tasks for more comprehensive predictions of personal entities in daily conversations. We first demonstrate that there are substantial limitations to the evaluation methods of previous work, namely that randomized assignment of samples to training and test data substantially decreases the complexity of cloze-style reading comprehension. According to our analysis, replacing the random data split with a chronological data split reduces test accuracy on previous single-variable passage completion task from 72\% to 34\%, that leaves much more room to improve. Our proposed tasks extend the previous single-variable passage completion task by replacing more character mentions with variables. Several deep learning models are developed to validate these three tasks. A thorough error analysis is provided to understand the challenges and guide the future direction of this research.

Researchers have presented a new artificial intelligence bot that can predict whether a movie will be a critical and financial siccess by simply analyzing its plot. The team behind the AI presented their paper at the 2019 Storytelling Workshop held in Florence, Italy this month. They claimed that the AI can help movie producers decide which movies might be worth the investment. "As the size of investment for movie production grows bigger, the need for predicting a movie's success in early stages has increased," explained the study authors in the paper's abstract. "To enable a more earlier prediction of a movie's performance, we propose a deep-learning based approach to predict the success of a movie using only its plot summary text."

Artificial intelligence (AI) still can't see the future, but a new algorithm may come close: Using nothing but written movie summaries, the AI can consistently tell which films will play well--or rottenly--to critics and audiences. If the model can be further refined, it could one day help producers predict whether a movie will be a flop at the box office, before it's even made. To test several models, researchers used plot summaries of 42,306 movies from all over the world, many collected from Wikipedia. The models broke up the summaries by sentence and used something called sentiment analysis to analyze each one. Sentences considered "positive," such as "Thor loves his hammer," would receive a rating closer to one.

This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employed for the multi-label problem involved in the genre tagging task.Experiments with more than 250,000 movies show that employing the Gated Recurrent Units (GRU) neural networks for the probabilistic classification with learned probability threshold approach achieves the best result on the test set. The model attains a Jaccard Index of 50.0%, a F-score of 0.56, and a hit rate of 80.5%.

Well, Half-Life 3 is finally here. Or is it Half-Life 2: Episode 3? Hard to say, but in any case, it arrived exactly as expected: A whispered surprise, traveling in the wee hours of the night by word of mouth alone. Instead, longtime Valve writer Marc Laidlaw (ex-Valve writer as of last year) seems to have posted the hypothetical plot to a never-going-to-be-made Episode 3 on his blog. It's thinly disguised as "Epistle 3," and the character names and genders have been tweaked too. This forms the basis of Laidlaw's current deflection, which is that it's "a genderswapped snapshot of a dream I had many years ago." He also refers to it as "fanfic."