Video Storytelling: Textual Summaries for Events

Bridging vision and natural language is a longstanding goal in computer vision and multimedia research. While earlier works focus on generating a single-sentence description for visual content, recent works have studied paragraph generation. In this work, we introduce the problem of video storytelling, which aims at generating coherent and succinct stories for long videos. Video storytelling introduces new challenges, mainly due to the diversity of the story and the length and complexity of the video. We propose novel methods to address the challenges. First, we propose a context-aware framework for multimodal embedding learning, where we design a Residual Bidirectional Recurrent Neural Network to leverage contextual information from past and future. Second, we propose a Narrator model to discover the underlying storyline. The Narrator is formulated as a reinforcement learning agent which is trained by directly optimizing the textual metric of the generated story. We evaluate our method on the Video Story dataset, a new dataset that we have collected to enable the study. We compare our method with multiple state-of-the-art baselines, and show that our method achieves better performance, in terms of quantitative measures and user study.Comment: Published in IEEE Transactions on Multimedi

Discourse Level Factors for Sentence Deletion in Text Simplification

This paper presents a data-driven study focusing on analyzing and predicting sentence deletion -- a prevalent but understudied phenomenon in document simplification -- on a large English text simplification corpus. We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected. We reveal that professional editors utilize different strategies to meet readability standards of elementary and middle schools. To predict whether a sentence will be deleted during simplification to a certain level, we harness automatically aligned data to train a classification model. Evaluated on our manually annotated data, our best models reached F1 scores of 65.2 and 59.7 for this task at the levels of elementary and middle school, respectively. We find that discourse level factors contribute to the challenging task of predicting sentence deletion for simplification.Comment: Accepted in AAAI 2020. Adding more details on manual data annotatio

Automatic Image Captioning with Style

This thesis connects two core topics in machine learning, vision and language. The problem of choice is image caption generation: automatically constructing natural language descriptions of image content. Previous research into image caption generation has focused on generating purely descriptive captions; I focus on generating visually relevant captions with a distinct linguistic style. Captions with style have the potential to ease communication and add a new layer of personalisation. First, I consider naming variations in image captions, and propose a method for predicting context-dependent names that takes into account visual and linguistic information. This method makes use of a large-scale image caption dataset, which I also use to explore naming conventions and report naming conventions for hundreds of animal classes. Next I propose the SentiCap model, which relies on recent advances in artificial neural networks to generate visually relevant image captions with positive or negative sentiment. To balance descriptiveness and sentiment, the SentiCap model dynamically switches between two recurrent neural networks, one tuned for descriptive words and one for sentiment words. As the first published model for generating captions with sentiment, SentiCap has influenced a number of subsequent works. I then investigate the sub-task of modelling styled sentences without images. The specific task chosen is sentence simplification: rewriting news article sentences to make them easier to understand. For this task I design a neural sequence-to-sequence model that can work with limited training data, using novel adaptations for word copying and sharing word embeddings. Finally, I present SemStyle, a system for generating visually relevant image captions in the style of an arbitrary text corpus. A shared term space allows a neural network for vision and content planning to communicate with a network for styled language generation. SemStyle achieves competitive results in human and automatic evaluations of descriptiveness and style. As a whole, this thesis presents two complete systems for styled caption generation that are first of their kind and demonstrate, for the first time, that automatic style transfer for image captions is achievable. Contributions also include novel ideas for object naming and sentence simplification. This thesis opens up inquiries into highly personalised image captions; large scale visually grounded concept naming; and more generally, styled text generation with content control

Adapting Automatic Summarization to New Sources of Information

English-language news articles are no longer necessarily the best source of information. The Web allows information to spread more quickly and travel farther: first-person accounts of breaking news events pop up on social media, and foreign-language news articles are accessible to, if not immediately understandable by, English-speaking users. This thesis focuses on developing automatic summarization techniques for these new sources of information. We focus on summarizing two specific new sources of information: personal narratives, first-person accounts of exciting or unusual events that are readily found in blog entries and other social media posts, and non-English documents, which must first be translated into English, often introducing translation errors that complicate the summarization process. Personal narratives are a very new area of interest in natural language processing research, and they present two key challenges for summarization. First, unlike many news articles, whose lead sentences serve as summaries of the most important ideas in the articles, personal narratives provide no such shortcuts for determining where important information occurs in within them; second, personal narratives are written informally and colloquially, and unlike news articles, they are rarely edited, so they require heavier editing and rewriting during the summarization process. Non-English documents, whether news or narrative, present yet another source of difficulty on top of any challenges inherent to their genre: they must be translated into English, potentially introducing translation errors and disfluencies that must be identified and corrected during summarization. The bulk of this thesis is dedicated to addressing the challenges of summarizing personal narratives found on the Web. We develop a two-stage summarization system for personal narrative that first extracts sentences containing important content and then rewrites those sentences into summary-appropriate forms. Our content extraction system is inspired by contextualist narrative theory, using changes in writing style throughout a narrative to detect sentences containing important information; it outperforms both graph-based and neural network approaches to sentence extraction for this genre. Our paraphrasing system rewrites the extracted sentences into shorter, standalone summary sentences, learning to mimic the paraphrasing choices of human summarizers more closely than can traditional lexicon- or translation-based paraphrasing approaches. We conclude with a chapter dedicated to summarizing non-English documents written in low-resource languages – documents that would otherwise be unreadable for English-speaking users. We develop a cross-lingual summarization system that performs even heavier editing and rewriting than does our personal narrative paraphrasing system; we create and train on large amounts of synthetic errorful translations of foreign-language documents. Our approach produces fluent English summaries from disdisfluent translations of non-English documents, and it generalizes across languages

Generación de resúmenes de videos basada en consultas utilizando aprendizaje de máquina y representaciones coordinadas

Video constitutes the primary substrate of information of humanity, consider the video data uploaded daily on platforms as YouTube: 300 hours of video per minute, video analysis is currently one of the most active areas in computer science and industry, which includes fields such as video classification, video retrieval and video summarization (VSUMM). VSUMM is a hot research field due to its importance in allowing human users to simplify the information processing required to see and analyze sets of videos, for example, reducing the number of hours of recorded videos to be analyzed by a security personnel. On the other hand, many video analysis tasks and systems requires to reduce the computational load using segmentation schemes, compression algorithms, and video summarization techniques. Many approaches have been studied to solve VSUMM. However, it is not a single solution problem due to its subjective and interpretative nature, in the sense that important parts to be preserved from the input video requires a subjective estimation of an importance sco- re. This score can be related to how interesting are some video segments, how close they represent the complete video, and how segments are related to the task a human user is performing in a given situation. For example, a movie trailer is, in part, a VSUMM task but related to preserving promising and interesting parts from the movie but not to be able to reconstruct the movie content from them, i.e., movie trailers contains interesting scenes but not representative ones. On the contrary, in a surveillance situation, a summary from the closed-circuit cameras needs to be representative and interesting, and in some situations related with some objects of interest, for example, if it is needed to find a person or a car. As written natural language is the main human-machine communication interface, recently some works have made advances in allowing to include textual queries in the VSUMM process which allows to guide the summarization process, in the sense that video segments related with the query are considered important. In this thesis, we present a computational framework to perform video summarization over an input video, which allows the user to input free-form sentences and keywords queries to guide the process by considering user intention or task intention, but also considering general objectives such as representativeness and interestingness. Our framework relies on the use of pre-trained deep visual and linguistic models, although we trained our visual-linguistic coordination model. We expect this model will be of interest in cases where VSUMM tasks requires a high degree of specification of user/task intentions with minimal training stages and rapid deployment.El video constituye el sustrato primario de información de la humanidad, por ejemplo, considere los datos de video subidos diariamente en plataformas cómo YouTube: 300 horas de video por minuto. El análisis de video es actualmente una de las áreas más activas en la informática y la industria, que incluye campos como la clasificación, recuperación y generación de resúmenes de video (VSUMM). VSUMM es un campo de investigación de alto dinamismo debido a su importancia al permitir que los usuarios humanos simplifiquen el procesamiento de la información requerido para ver y analizar conjuntos de videos, por ejemplo, reduciendo la cantidad de horas de videos grabados para ser analizados por un personal de seguridad. Por otro lado, muchas tareas y sistemas de análisis de video requieren reducir la carga computacional utilizando esquemas de segmentación, algoritmos de compresión y técnicas de VSUMM. Se han estudiado muchos enfoques para abordar VSUMM. Sin embargo, no es un problema de solución única debido a su naturaleza subjetiva e interpretativa, en el sentido de que las partes importantes que se deben preservar del video de entrada, requieren una estimación de una puntuación de importancia. Esta puntuación puede estar relacionada con lo interesantes que son algunos segmentos de video, lo cerca que representan el video completo y con cómo los segmentos están relacionados con la tarea que un usuario humano está realizando en una situación determinada. Por ejemplo, un avance de película es, en parte, una tarea de VSUMM, pero esta ́ relacionada con la preservación de partes prometedoras e interesantes de la película, pero no con la posibilidad de reconstruir el contenido de la película a partir de ellas, es decir, los avances de películas contienen escenas interesantes pero no representativas. Por el contrario, en una situación de vigilancia, un resumen de las cámaras de circuito cerrado debe ser representativo e interesante, y en algunas situaciones relacionado con algunos objetos de interés, por ejemplo, si se necesita para encontrar una persona o un automóvil. Dado que el lenguaje natural escrito es la principal interfaz de comunicación hombre-máquina, recientemente algunos trabajos han avanzado en permitir incluir consultas textuales en el proceso VSUMM lo que permite orientar el proceso de resumen, en el sentido de que los segmentos de video relacionados con la consulta se consideran importantes. En esta tesis, presentamos un marco computacional para realizar un resumen de video sobre un video de entrada, que permite al usuario ingresar oraciones de forma libre y consultas de palabras clave para guiar el proceso considerando la intención del mismo o la intención de la tarea, pero también considerando objetivos generales como representatividad e interés. Nuestro marco se basa en el uso de modelos visuales y linguísticos profundos pre-entrenados, aunque también entrenamos un modelo propio de coordinación visual-linguística. Esperamos que este marco computacional sea de interés en los casos en que las tareas de VSUMM requieran un alto grado de especificación de las intenciones del usuario o tarea, con pocas etapas de entrenamiento y despliegue rápido.MincienciasDoctorad