Advances and Trends in Multimodal Digital Content Generation and Automatic Text Summarization

Multimodal digital content generation refers to AI techniques that synthesize images, video, audio, text, music and other media. Breakthroughs such as GPT‑3 in natural language processing and Deepfake in computer vision have made this a hot research direction, with applications in virtual try‑on, AI‑generated music, marketing copy, poetry, stylized calligraphy, and text‑image cross‑generation.

The article focuses on automatic text summarization as a lens to discuss the progress and practice of multimodal content generation at JD AI Research Institute in 2020, and to explore future trends and scenarios.

Evolution of Automatic Summarization – Originating in the 1950s, summarization aims to produce a concise text that captures the most important information from a given document or multimodal input. Early work was extractive, selecting existing sentences or phrases. Since 2015, generative approaches based on Seq2Seq, Pointer‑Generator, and, more recently, Transformer‑based pretrained language models (e.g., MASS, UniLM, T5, ProphetNet) have become dominant, driven by large datasets such as Gigaword, CNN‑DailyMail, XSUM, and MSMO.

Technology Trend 1: Pretrained Language Models – Generative pre‑training (e.g., MASS, UniLM, T5, ProphetNet) consistently pushes summarization performance forward.

Technology Trend 2: Transformers as the Mainstream Generation Model – Transformers have surpassed RNN‑based Seq2Seq models in many generation tasks. Research also addresses representation degeneration in the output embedding space by applying spectrum control via singular‑value decomposition, improving translation and summarization quality.

Technology Trend 3: Knowledge‑Infused Generation – Incorporating knowledge graphs and external knowledge improves faithfulness of generated e‑commerce copy. Techniques include attention‑guided copy mechanisms, keyword‑guided abstractive models, and multimodal knowledge‑graph augmentation, achieving higher ROUGE scores and better human judgments.

Technology Trend 4: Multimodal‑Knowledge Joint Modeling – Combining visual and textual modalities with knowledge helps resolve ambiguities and enrich content. Examples include multimodal product summarization that fuses product descriptions and images, multimodal news summarization with selective encoding of global, local, and entity visual features, and multimodal‑guided image selection for better text‑image summaries.

Industrial Practice – JD has commercialized these innovations into the “Pinchuang” AI copy‑writing platform, which generates high‑quality, diverse, and faithful product descriptions. Highlights: >90% human‑review pass rate, support for 3,000+ product categories, 40% higher click‑through rate than professional writers, >90% cost reduction, and over 30 patented inventions with a top‑level technology award.

Recommended Reading – Links to related articles on future technology trends, code tutorials, and R&D efficiency are provided for further exploration.