  In recent years, abstractive text summarization with multimodal inputs has
started drawing attention due to its ability to accumulate information from
different source modalities and generate a fluent textual summary. However,
existing methods use short videos as the visual modality and short summary as
the ground-truth, therefore, perform poorly on lengthy videos and long
ground-truth summary. Additionally, there exists no benchmark dataset to
generalize this task on videos of varying lengths. In this paper, we introduce
AVIATE, the first large-scale dataset for abstractive text summarization with
videos of diverse duration, compiled from presentations in well-known academic
conferences like NDSS, ICML, NeurIPS, etc. We use the abstract of corresponding
research papers as the reference summaries, which ensure adequate quality and
uniformity of the ground-truth. We then propose FLORAL, a factorized
multi-modal Transformer based decoder-only language model, which inherently
captures the intra-modal and inter-modal dynamics within various input
modalities for the text summarization task. FLORAL utilizes an increasing
number of self-attentions to capture multimodality and performs significantly
better than traditional encoder-decoder based networks. Extensive experiments
illustrate that FLORAL achieves significant improvement over the baselines in
both qualitative and quantitative evaluations on the existing How2 dataset for
short videos and newly introduced AVIATE dataset for videos with diverse
duration, beating the best baseline on the two datasets by $1.39$ and $2.74$
ROUGE-L points respectively.
