Multimodal Summarization Research Articles

Heterogeneous graphormer for extractive multimodal summarization

Heterogeneous graphormer for extractive multimodal summarization

Multimodal Cross-lingual Summarization for Videos: A Revisit in Knowledge Distillation Induced Triple-stage Training Method.

Multimodal summarization (MS) for videos aims to generate summaries from multi-source information (e.g., video and text transcript), and this technique has made promising progress recently. However, existing works are limited to monolingual video scenarios, overlooking the demands of non-native language video viewers to understand cross-lingual videos in practical applications. It stimulates us to introduce multimodal cross-lingual summarization for videos (MCLS), which aims at generating cross-lingual summarization from multimodal input of videos. Considering the challenge of high annotation cost and resource constraints in MCLS, we propose a knowledge distillation (KD) induced triple-stage training method to assist MCLS by transferring knowledge from abundant monolingual MS data to those data with insufficient volumes. In the triple-stage training method, a video-guided dual fusion network (VDF) is designed as the backbone network to integrate multimodal and cross-lingual information through different fusion strategies of encoder and decoder; what's more, we propose two cross-lingual knowledge distillation strategies: adaptive pooling distillation and language-adaptive warping distillation (LAWD). These strategies are tailored for distillation objects (i.e., encoder-level and vocab-level KD) to facilitate effective knowledge transfer across cross-lingual sequences of varying lengths between MS and MCLS models. Specifically, to tackle the challenge of unequal length of parallel cross-language sequences in KD, our proposed LAWD can directly conduct cross-language distillation while keeping the language feature shape unchanged to reduce potential information loss. We meticulously annotated the How2-MCLS dataset based on the How2 dataset to simulate the MCLS scenario. The experimental results show that the proposed method achieves competitive performance compared to strong baselines, and can bring substantial performance improvements to MCLS models by transferring knowledge from the MS model.

Abstractive text summarization: State of the art, challenges, and improvements

Specifically focusing on the landscape of abstractive text summarization, as opposed to extractive techniques, this survey presents a comprehensive overview, delving into state-of-the-art techniques, prevailing challenges, and prospective research directions. We categorize the techniques into traditional sequence-to-sequence models, pre-trained large language models, reinforcement learning, hierarchical methods, and multi-modal summarization. Unlike prior works that did not examine complexities, scalability and comparisons of techniques in detail, this review takes a comprehensive approach encompassing state-of-the-art methods, challenges, solutions, comparisons, limitations and charts out future improvements — providing researchers an extensive overview to advance abstractive summarization research. We provide vital comparison tables across techniques categorized — offering insights into model complexity, scalability and appropriate applications. The paper highlights challenges such as inadequate meaning representation, factual consistency, controllable text summarization, cross-lingual summarization, and evaluation metrics, among others. Solutions leveraging knowledge incorporation and other innovative strategies are proposed to address these challenges. The paper concludes by highlighting emerging research areas like factual inconsistency, domain-specific, cross-lingual, multilingual, and long-document summarization, as well as handling noisy data. Our objective is to provide researchers and practitioners with a structured overview of the domain, enabling them to better understand the current landscape and identify potential areas for further research and improvement.

Align vision-language semantics by multi-task learning for multi-modal summarization

Align vision-language semantics by multi-task learning for multi-modal summarization

Multization: Multi-Modal Summarization Enhanced by Multi-Contextually Relevant and Irrelevant Attention Alignment

This article focuses on the task of Multi-Modal Summarization with Multi-Modal Output for China JD.COM e-commerce product description containing both source text and source images. In the context learning of multi-modal (text and image) input, there exists a semantic gap between text and image, especially in the cross-modal semantics of text and image. As a result, capturing shared cross-modal semantics earlier becomes crucial for multi-modal summarization. However, when generating the multi-modal summarization, based on the different contributions of input text and images, the relevance and irrelevance of multi-modal contexts to the target summary should be considered, so as to optimize the process of learning cross-modal context to guide the summary generation process and to emphasize the significant semantics within each modality. To address the aforementioned challenges, Multization has been proposed to enhance multi-modal semantic information by multi-contextually relevant and irrelevant attention alignment. Specifically, a Semantic Alignment Enhancement mechanism is employed to capture shared semantics between different modalities (text and image), so as to enhance the importance of crucial multi-modal information in the encoding stage. Additionally, the IR-Relevant Multi-Context Learning mechanism is utilized to observe the summary generation process from both relevant and irrelevant perspectives, so as to form a multi-modal context that incorporates both text and image semantic information. The experimental results in the China JD.COM e-commerce dataset demonstrate that the proposed Multization method effectively captures the shared semantics between the input source text and source images, and highlights essential semantics. It also successfully generates the multi-modal summary (including image and text) that comprehensively considers the semantics information of both text and image.

DIUSum: Dynamic Image Utilization for Multimodal Summarization

Existing multimodal summarization approaches focus on fusing image features in the encoding process, ignoring the individualized needs for images when generating different summaries. However, whether intuitively or empirically, not all images can improve summary quality. Therefore, we propose a novel Dynamic Image Utilization framework for multimodal Summarization (DIUSum) to select and utilize valuable images for summarization. First, to predict whether an image helps produce a high-quality summary, we propose an image selector to score the usefulness of each image. Second, to dynamically utilize the multimodal information, we incorporate the hard and soft guidance from the image selector. Under the guidance, the image information is plugged into the decoder to generate a summary. Experimental results have shown that DIUSum outperforms multiple strong baselines and achieves SOTA on two public multimodal summarization datasets. Further analysis demonstrates that the image selector can reflect the improved level of summary quality brought by the images.

CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare

In the era of modern healthcare, swiftly generating medical question summaries is crucial for informed and timely patient care. Despite the increasing complexity and volume of medical data, existing studies have focused solely on text-based summarization, neglecting the integration of visual information. Recognizing the untapped potential of combining textual queries with visual representations of medical conditions, we introduce the Multimodal Medical Question Summarization (MMQS) Dataset. This dataset, a major contribution of our work, pairs medical queries with visual aids, facilitating a richer and more nuanced understanding of patient needs. We also propose a framework, utilizing the power of Contrastive Language Image Pretraining(CLIP) and Large Language Models(LLMs), consisting of four modules that identify medical disorders, generate relevant context, filter medical concepts, and craft visually aware summaries. Our comprehensive framework harnesses the power of CLIP, a multimodal foundation model, and various general-purpose LLMs, comprising four main modules: the medical disorder identification module, the relevant context generation module, the context filtration module for distilling relevant medical concepts and knowledge, and finally, a general-purpose LLM to generate visually aware medical question summaries. Leveraging our MMQS dataset, we showcase how visual cues from images enhance the generation of medically nuanced summaries. This multimodal approach not only enhances the decision-making process in healthcare but also fosters a more nuanced understanding of patient queries, laying the groundwork for future research in personalized and responsive medical care. Disclaimer: The article features graphic medical imagery, a result of the subject's inherent requirements.

Multi-task Hierarchical Heterogeneous Fusion Framework for multimodal summarization

With the rise of multimedia content on the internet, Multimodal Summarization has become a challenging task to help individuals grasp vital information fast. However, previous methods mainly learn the different modalities indistinguishably, which is ineffective in capturing the fine-grained content and hierarchical correlation in multimodal articles. To resolve the present problem, this paper proposes a Multi-task Hierarchical Heterogeneous Fusion Framework (MHHF) to learn the hierarchical structure and heterogeneous correlation existing in the multimodal data. Specifically, we propose a Hierarchical Cross-modality Feature Fusion module to progressively explore the different levels of interaction from object-word features to sentence-scene features. Besides, a Multi-task Cross-modality Decoder is constructed to coalesce different levels of features with three sub-tasks, i.e., Abstractive Summary Generation, Relevant Image Selection, and Extractive Summary Generation. We conduct extensive experiments on three datasets, i.e., MHHF-dataset, CNN, and Daily Mail, which consist of 62880, 1970, and 203 multimodal articles, respectively. Our method achieves state-of-the-art performance on most metrics. Moreover, MHHF consistently outperforms the baseline model on MHHF-dataset by 5.88%, 4.41%, and 0.4% of Rouge-1, Rouge-2, and Rouge-L for the abstractive summarization task. Ablation studies show that both Hierarchical Cross-modality Feature Fusion and Multi-task Cross-modality Decoder can improve the quality of multimodal summarization output. Further diversity analysis and human evaluation also demonstrate that MHHF can generate more informative and fluent summaries.

SCT: Summary Caption Technique for Retrieving Relevant Images in Alignment with Multimodal Abstractive Summary

This work proposes an efficient Summary Caption Technique that considers the multimodal summary and image captions as input to retrieve the correspondence images from the captions that are highly influential to the multimodal summary. Matching a multimodal summary with an appropriate image is a challenging task in computer vision and natural language processing. Merging in these fields is tedious, though the research community has steadily focused on cross-modal retrieval. These issues include the visual question-answering, matching queries with the images, and semantic relationship matching between two modalities for retrieving the corresponding image. Relevant works consider questions to match the relationship of visual information and object detection and to match the text with visual information and employing structural-level representation to align the images with the text. However, these techniques are primarily focused on retrieving the images to text or for image captioning. But less effort has been spent on retrieving relevant images for the multimodal summary. Hence, our proposed technique extracts and merge features in the Hybrid Image Text layer and captions in the semantic embeddings with word2vec where the contextual features and semantic relationships are compared and matched with each vector between the modalities, with cosine semantic similarity. In cross-modal retrieval, we achieve top five related images and align the relevant images to the multimodal summary that achieves the highest cosine score among the retrieved images. The model has been trained with seq-to-seq modal with 100 epochs, besides reducing the information loss by the sparse categorical cross entropy. Further, experimenting with the multimodal summarization with multimodal output dataset, in cross-modal retrieval, helps to evaluate the quality of image alignment with an image-precision metric that demonstrate the best results.

Multimodal Abstractive Summarization using bidirectional encoder representations from transformers with attention mechanism

In recent decades, abstractive text summarization using multimodal input has attracted many researchers due to the capability of gathering information from various sources to create a concise summary. However, the existing methodologies based on multimodal summarization provide only a summary for the short videos and poor results for the lengthy videos. To address the aforementioned issues, this research presented the Multimodal Abstractive Summarization using Bidirectional Encoder Representations from Transformers (MAS-BERT) with an attention mechanism. The purpose of the video summarization is to increase the speed of searching for a large collection of videos so that the users can quickly decide whether the video is relevant or not by reading the summary. Initially, the data is obtained from the publicly available How2 dataset and is encoded using the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder and the Long Short Term Memory (LSTM) encoder. The textual data which is embedded in the embedding layer is encoded using a bidirectional GRU encoder and the features with audio and video data are encoded with LSTM encoder. After this, BERT based attention mechanism is used to combine the modalities and finally, the BI-GRU based decoder is used for summarizing the multimodalities. The results obtained through the experiments that show the proposed MAS-BERT has achieved a better result of 60.2 for Rouge-1 whereas, the existing Decoder-only Multimodal Transformer (D-MmT) and the Factorized Multimodal Transformer based Decoder Only Language model (FLORAL) has achieved 49.58 and 56.89 respectively. Our work facilitates users by providing better contextual information and user experience and would help video-sharing platforms for customer retention by allowing users to search for relevant videos by looking at its summary.

MMSFT: Multilingual Multimodal Summarization by Fine-Tuning Transformers

MMSFT: Multilingual Multimodal Summarization by Fine-Tuning Transformers

Multi-modal anchor adaptation learning for multi-modal summarization

In this paper, we focus on analyzing the relationship between the input of source text and source image, and then through the integration and generalization of the multi-modal information (e.g., texts and images), outputs the multi-modal summarization including text and image. However, existing multi-modal summarization methods face several challenges: (1) Different modalities exist in different semantic spaces, and expressing the multi-modal information of source modalities in a similar semantic representation space is crucial. (2) For the result of text summarization, it is crucial to explore how to capture both the differences and similarities within the source modalities, which can reduce redundancy to improve the quality of text summarization. In order to overcome the challenge above, Multi-Modal Anchor Adaptation Learning for Multi-Modal Summarization (MA-Sum) has been proposed. Specifically, MA-Sum employs a novel and highly efficient image anchor selection method, which selects the object sample containing the richest image information as the image anchor. Simultaneously, it carefully selects the text sentence closely intertwined with the image semantics to serve as the language anchor. Therefore, the multi-modal anchor can be seen as a bridge for multi-modal alignment to alleviate the semantic gap between textual and visual. Moreover, based on the distance between the anchors and the semantic information in the respective modal, the positive and negative semantic information of each modal will be distinguished. Based on negative semantic information, the counterfactual learning mechanism is constructed to optimize the result of multi-modal summarization. Finally, the process of multi-modal features interaction is optimized by image summary which is chosen by using multi-modal anchors. According to the experimental results, compared with the state-of-art multi-modal summarization, our proposed MA-Sum can be optimized in terms of summarization consistency and completeness, so as to obtain the optimal multi-modal summarization metric.

Multimodal text summarization with evaluation approaches

Multimodal text summarization with evaluation approaches

Topic-guided abstractive multimodal summarization with multimodal output

Topic-guided abstractive multimodal summarization with multimodal output

A novel multi-modal neural network approach for dynamic and generic sports video summarization

Video Summarization is a video compression/compaction technique to create a shorter yet informative version of original video. Video summarization has offered solutions to plenty of media, user and engineering applications. Though sports video summarization has been an active research topic for some time; there still exists a void for multi-modal, dynamic, generic and domain knowledge based approach for Cricket Sport video summarization. This paper presents a multi-modal video summarization approach to summarize Cricket sport videos. This work captures the domain knowledge acquired from multi-modal (audio-visual) cues. A dual neural network architecture pipeline is proposed to dynamically segment and dynamically summarize Cricket videos for generic target audience. The former Neural Network is grounded on Cricket bowling activity (visual feature) for dynamic video segmentation of Cricket videos. The segments are then forwarded to the latter Neural Network for identification of key segments. The key segment detection module relies on Audio analysis of Cricket video stream to identify exciting, content representative and informative segments as per Cricket domain. Experimental analysis on two novel proposed benchmark datasets, i.e. DPCS (Delivery Play Cricket Sport) image dataset and EXINP (Excited Interval Normal Play) Cricket Dataset (audio based) shows promising results. The results indicate that the proposed multi-modal approach generates exciting, content representative, informative, generic and dynamic summary incorporating domain knowledge of the sport.

A Survey on Multi-modal Summarization

The new era of technology has brought us to the point where it is convenient for people to share their opinions over an abundance of platforms. These platforms have a provision for the users to express themselves in multiple forms of representations, including text, images, videos, and audio. This, however, makes it difficult for users to obtain all the key information about a topic, making the task of automatic multi-modal summarization (MMS) essential. In this article, we present a comprehensive survey of the existing research in the area of MMS, covering various modalities such as text, image, audio, and video. Apart from highlighting the different evaluation metrics and datasets used for the MMS task, our work also discusses the current challenges and future directions in this field.

Learning a holistic and comprehensive code representation for code summarization

Code summarization is the task of describing the function of code snippets in natural language, which benefits program comprehension and boosts software productivity. Despite lots of effort made by previous studies, existing models are not comprehensive enough to represent code, and yet the literature does not consider to model source code with API usage from a holistic perspective. To this end, this paper proposes a novel multi-modal code summarization approach called HCCS (Learning a Holistic and Comprehensive code representation for Code Summarization). We first design a neural network based on the graph attention mechanism to encode API Context Graph (ACG), which highlights holistic information of source code. Then, a multi-modal framework with a tree encoder for Abstract Syntax Tree (AST) and a code encoder for code tokens is incorporated to learn a more comprehensive code representation. Afterwards, we propose a fusing layer to integrate the encodings, which are then passed to a joint-decoder to generate summaries. The experimental results show that HCCS achieves better performance than the state of the arts (i.e., HCCS scores 9.5% higher in terms of BLEU metric and 11.46% in terms of BERTScore). As a result, HCCS is an effective approach to generate high-quality summaries.

Vision Enhanced Generative Pre-trained Language Model for Multimodal Sentence Summarization

Multimodal sentence summarization (MMSS) is a new yet challenging task that aims to generate a concise summary of a long sentence and its corresponding image. Although existing methods have gained promising success in MMSS, they overlook the powerful generation ability of generative pre-trained language models (GPLMs), which have shown to be effective in many text generation tasks. To fill this research gap, we propose to using GPLMs to promote the performance of MMSS. Notably, adopting GPLMs to solve MMSS inevitably faces two challenges: 1) What fusion strategy should we use to inject visual information into GPLMs properly? 2) How to keep the GPLM’s generation ability intact to the utmost extent when the visual feature is injected into the GPLM. To address these two challenges, we propose a vision enhanced generative pre-trained language model for MMSS, dubbed as Vision-GPLM. In Vision-GPLM, we obtain features of visual and textual modalities with two separate encoders and utilize a text decoder to produce a summary. In particular, we utilize multi-head attention to fuse the features extracted from visual and textual modalities to inject the visual feature into the GPLM. Meanwhile, we train Vision-GPLM in two stages: the vision-oriented pre-training stage and fine-tuning stage. In the vision-oriented pre-training stage, we particularly train the visual encoder by the masked language model task while the other components are frozen, aiming to obtain homogeneous representations of text and image. In the fine-tuning stage, we train all the components of Vision-GPLM by the MMSS task. Extensive experiments on a public MMSS dataset verify the superiority of our model over existing baselines.

Abstractive Summarization for Video: A Revisit in Multistage Fusion Network With Forget Gate

Multimodal abstractive summarization for videos is an emerging task, aiming to generate a summary from multi-source information (i.e. video, audio transcript). The challenge is how to merge multimodal long sequences to capture rich semantic information without allowing possible noise from either lengthy modal sequence to degrade the other modality and thus hurt the entire model. To address the issues, we propose a multistage fusion network with forget gate (MFFG), which selectively integrates multi-source information through the cross-fusion in encoding and hierarchical fusion in decoding between modalities, and design a fusion forget gate module to suppress the flow of potential multimodal noise of multi-source long sequence. Meanwhile, considering that the source text in this task is lengthy and has the same distribution as the output summary text, we inherit the partial structure of the MFFG model and again propose its variant, single-stage fusion network with forget gate (SFFG), which simplifies the fusion schema, and leverages the long source text to enhance the representation of the target summary. Experimental results on How2 dataset and How2-300 dataset demonstrate the superiority of the two multimodal fusion methods. Further, we provide a version of ASR transcription data of How2 dataset to evaluate model performance under noisy scenarios, and experimental results show obvious advantages of our proposed models over prior systems.

Extractive text-image summarization with relation-enhanced graph attention network

Multi-modal summarization with multi-modal output (MSMO) aims to generate multi-modal summaries for a multi-modal document to improve readability of summaries by making use of information of different modalities. Most existing Seq2Seq-based MSMO models cannot well capture multi-modal relations which are significant for generating high-quality multi-modal summaries. To address this issue, this paper proposes a relation-enhanced graph attention network for extractive text-image summarization (ReGAT-Summ) to capture inter-modal and intra-modal relations in the multi-modal document. Firstly, a multi-modal graph is constructed from the document. Then, node representations are calculated by proposed graph neural network. Finally, a sentence-image selector is trained to select salient sentences and images, which are further aligned by training. To our knowledge, we are the first to explore the graph-based model for MSMO. Experiments on two news datasets E-DailyMail and NYTime800k demonstrate that ReGAT-Summ achieves the state-of-the-art performance in terms of automatic metrics and human evaluations.