In this paper, we focus on video-to-text summarization and investigate how to best utilize multimodal information for summarizing long inputs (e. g., an hour-long TV show) into long outputs (e. g., a multi-sentence summary).
Large pre-trained language models (LMs) have demonstrated impressive capabilities in generating long, fluent text; however, there is little to no analysis on their ability to maintain entity coherence and consistency.
Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie.
We summarize full-length movies by creating shorter videos containing their most informative scenes.
Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront.
According to screenwriting theory, turning points (e. g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e. g., setup, complications, aftermath).
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets".
no code implementations • • Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Mal, Nikolaos rakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth Narayanan, Alex Potamianos, ros
In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter.