Hierarchical3D Adapters for Long Video-to-text Summarization

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). We extend SummScreen (Chen et al., 2021), a dialogue summarization dataset consisting of transcripts of TV episodes with reference summaries, and create a multimodal variant by collecting corresponding full-length videos. We incorporate multimodal information into a pre-trained textual summarizer efficiently using adapter modules augmented with a hierarchical structure while tuning only 3.8\% of model parameters. Our experiments demonstrate that multimodal information offers superior performance over more memory-heavy and fully fine-tuned textual summarization methods

Система реферирования мультимодальной информации

Розглянуто процес реферування мультимодальної інформації. Тобто початкова інформація може представлятися у вигляді тексту, зображення, аудіо або відео. Пропонується модель процесу реферування, поліпшена за рахунок введення додаткового етапу, що дозволяє обробляти мультимодальну інформацію. Наводяться алгоритм функціонування системи реферування на цьому етапі та модель процесу перетворення початкового документа у внутрішній формат системи.Рассмотрен процесс реферирования мультимодальной информации. То есть исходная информация может представляться в виде текста, изображения, аудио или видео. Предлагается модель процесса реферирования, улучшенная за счёт введения дополнительного этапа, позволяющего обрабатывать мультимодальную информацию. Приводятся алгоритм функционирования системы реферирования на этом этапе и модель процесса преобразования исходного документа во внутренний формат системы.This paper considers the process of multimodal information summarization. Input information can appear as a text, image, audio or video. There had been suggested the model of summarization process, improved by introducing of the additional stage, which allows to process multimodal information. The algorithm of the summarization system functioning on mentioned stage and model of transformation process of input document to the system's internal format had also been described

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

Query-Based Summarization using Rhetorical Structure Theory

Research on Question Answering is focused mainly on classifying the question type and finding the answer. Presenting the answer in a way that suits the user’s needs has received little attention. This paper shows how existing question answering systems—which aim at finding precise answers to questions—can be improved by exploiting summarization techniques to extract more than just the answer from the document in which the answer resides. This is done using a graph search algorithm which searches for relevant sentences in the discourse structure, which is represented as a graph. The Rhetorical Structure Theory (RST) is used to create a graph representation of a text document. The output is an extensive answer, which not only answers the question, but also gives the user an opportunity to assess the accuracy of the answer (is this what I am looking for?), and to find additional information that is related to the question, and which may satisfy an information need. This has been implemented in a working multimodal question answering system where it operates with two independently developed question answering modules

D2^2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization

Many-to-many multimodal summarization (M$^3$S) task aims to generate summaries in any language with document inputs in any language and the corresponding image sequence, which essentially comprises multimodal monolingual summarization (MMS) and multimodal cross-lingual summarization (MXLS) tasks. Although much work has been devoted to either MMS or MXLS and has obtained increasing attention in recent years, little research pays attention to the M$^3$S task. Besides, existing studies mainly focus on 1) utilizing MMS to enhance MXLS via knowledge distillation without considering the performance of MMS or 2) improving MMS models by filtering summary-unrelated visual features with implicit learning or explicitly complex training objectives. In this paper, we first introduce a general and practical task, i.e., M$^3$S. Further, we propose a dual knowledge distillation and target-oriented vision modeling framework for the M$^3$S task. Specifically, the dual knowledge distillation method guarantees that the knowledge of MMS and MXLS can be transferred to each other and thus mutually prompt both of them. To offer target-oriented visual features, a simple yet effective target-oriented contrastive objective is designed and responsible for discarding needless visual information. Extensive experiments on the many-to-many setting show the effectiveness of the proposed approach. Additionally, we will contribute a many-to-many multimodal summarization (M$^3$Sum) dataset.Comment: EMNLP 2023 Finding

Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

A novel user-centered design for personalized video summarization

In the past, several automatic video summarization systems had been proposed to generate video summary. However, a generic video summary that is generated based only on audio, visual and textual saliencies will not satisfy every user. This paper proposes a novel system for generating semantically meaningful personalized video summaries, which are tailored to the individual user's preferences over video semantics. Each video shot is represented using a semantic multinomial which is a vector of posterior semantic concept probabilities. The proposed system stitches video summary based on summary time span and top-ranked shots that are semantically relevant to the user's preferences. The proposed summarization system is evaluated using both quantitative and subjective evaluation metrics. The experimental results on the performance of the proposed video summarization system are encouraging