This article surveys automatic text summarization, detailing system classifications, extractive, abstractive and hybrid techniques, notable recent research, multi‑document and cross‑lingual challenges, major datasets, evaluation metrics, and promising future research avenues in the field.
This article provides a comprehensive overview of automatic text summarization, covering extractive, abstractive, and hybrid methods, system classifications, applications, datasets, evaluation metrics, and future research directions within the field of artificial intelligence.
This article explains the importance of text generation, introduces the PEGASUS model’s gap‑sentence pre‑training for abstractive summarization, and shows how the EasyNLP framework integrates PEGASUS and other Chinese and English summarization models with step‑by‑step installation, data preparation, and training commands.
This article reviews contemporary text summarization methods, covering extractive approaches such as TextRank and clustering, abstractive models like Seq2Seq with attention, pointer‑generator networks, and recent pre‑trained transformers including BART, CPT and PEGASUS, highlighting their strengths, limitations, and combined strategies.
This tutorial demonstrates how to create a lightweight Streamlit web app in Python that lets users select and run common NLP services—sentiment analysis, named‑entity recognition, and text summarization—by integrating libraries such as TextBlob, spaCy, and Gensim, with clear code examples and visual output.
The article reviews recent research on multimodal digital content generation and automatic text summarization, outlining the evolution from extractive to abstractive methods, highlighting four key technology trends such as pretrained language models, transformer dominance, knowledge‑enhanced generation, and multimodal‑knowledge joint modeling, and describing an industrial e‑commerce application built on these advances.
This article surveys the rapid proliferation of BERT-based research over the past six months, analyzing its impact on various NLP tasks such as question answering, information retrieval, dialog systems, summarization, data augmentation, classification, and sequence labeling, while also discussing the model's strengths, limitations, and future research opportunities.
This article details Alibaba.com engineers' application of reinforcement learning, attention mechanisms, and weakly supervised techniques to extract product summaries, improve content quality, and significantly raise SEO rankings, supported by offline experiments, online A/B testing, and future research directions.
This article investigates the challenge of generating concise e‑commerce product short titles by formalizing it as a constrained text‑summarization task, proposes a Multi‑Source Pointer Network that leverages both title and background knowledge encoders, and demonstrates its superiority through extensive offline and online experiments.
This article presents a multi‑source pointer network approach for generating concise, brand‑preserving product short titles in e‑commerce, detailing problem formalization, model architecture, extensive offline and online experiments, and demonstrating significant improvements over traditional truncation and seq2seq baselines.
This paper proposes a multi‑task learning approach that compresses overly long e‑commerce product titles into concise short titles using a Pointer Network, while simultaneously generating user search queries with an attention‑based encoder‑decoder, achieving higher readability, informativeness, and conversion rates than traditional methods.
Google Brain’s open‑source TensorFlow model tackles long‑text summarization by extracting key information and generating concise headlines, demonstrating state‑of‑the‑art extractive and abstractive techniques, with released code, hyper‑parameter details, and examples that illustrate its performance on news articles.
