Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding. It plays an essential role in real-world applications, i.e., automatically generating cover images and titles for news articles or providing introductions to online videos. However, existing methods extract features from the whole video and article and use fusion methods to select the representative one, thus usually ignoring the critical structure and varying semantics with video/document. In this work, we propose a Semantics-Consistent Cross-domain Summarization (SCCS) model based on optimal transport alignment with visual and textual segmentation. Our method first decomposes both videos and articles into segments in order to capture the structural semantics, and then follows a cross-domain alignment objective with optimal transport distance, which leverages multimodal interaction to match and select the visual and textual summary. We evaluated our method on three MSMO datasets, and achieved performance improvement by 8% & 6% of textual and 6.6% &5.7% of video summarization, respectively, which demonstrated the effectiveness of our method in producing high-quality multimodal summaries.

We propose an asymmetric encoder-decoder structure, which keeps an RNN as the encoder and has a CNN as the decoder, and the model only explores the subsequent context information as the supervision. The asymmetry in both model architecture and training pair reduces a large amount of the training time. The contribution of our work is summarized as 1. We design experiments to show that an autoregressive decoder or an RNN decoder is not necessary for the encoder-decoder type of models in terms of learning sentence representations, and based on our results, we present 2 findings. 2. The two interesting findings lead to our final model design, which has an RNN encoder and a CNN decoder, and it learns to encode the current sentence and decode the subsequent contiguous words all at once. 3. With a suite of techniques, our model performs good on downstream tasks and can be trained efficiently on a large unlabelled corpus.

We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn’t aid our model to perform better, while it hurts the performance of the skip-thought model.