LCSTS: A Large Scale Chinese Short Text Summarization Dataset

By Baotian Hu, Qingcai Chen, Fangze Zhu. 2015

368 citations recorded by the source corpus.

Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public {this http URL}. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. We also manually tagged the relevance of 10,666 short summaries with their corresponding short texts. Based on the corpus, we introduce recurrent neural network for the summary generation and achieve promising results, which not only shows the usefulness of the proposed corpus for short text summarization research, but also provides a baseline for further research on this topic.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond (2016, methodology): “Our work employs the same framework as (Hu et al., 2015), but we make the following contributions: (i) We show that the attentional encoder-decoder architecture outperforms state-of-the-art systems on two different English corpora.”
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021, neutral): “LCSTS (Hu et al., 2015) 14.”
WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization (2020, background): “Zhu et al. (2019) use round-trip translation of”
MLSUM: The Multilingual Summarization Corpus (2020, methodology): “LCSTS The Large Scale Chinese Short Text Summarization Dataset (Hu et al., 2015) is built from 2 million short texts from the Sina Weibo microblogging platform.”
TL;DR: Mining Reddit to Learn Automatic Summarization (2017, methodology): “But with recent advancements in the ﬁeld of deep learning, new ground was broken using various kinds of neural network models (Rush et al., 2015; Hu et al., 2015; Chopra et al., 2016; See et al., 2017).”
Learning to Extract Coherent Summary via Deep Reinforcement Learning (2018, background): “Most of the recent works on abstractive summarization focus on headline generation from one paragraph (Rush, Chopra, and Weston 2015) or several sentences (Hu, Chen, and Zhu 2015) by using sequence-to-sequence architectures borrowed from neural machine translation.”
Incorporating Copying Mechanism in Sequence-to-Sequence Learning (2016, methodology): “It has been recently formulated as a Seq2Seq learning problem in (Rush et al., 2015; Hu et al., 2015), which essentially gives abstractive summarization since the summary is generated based on a representation of the document.”
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks (2018, background): “Hu et al. (2015) propose the LCSTS dataset as a collection of Chinese microblog’s short text each paired with a summary.”
Neural Headline Generation with Minimum Risk Training (2016, methodology): “2The website of Sina Weibo is http://weibo.com/ The LCSTS consists of three parts.”
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation (2021, methodology): “• Text Summarization: LCSTS [10] • Question Generation:KBQG 16, DuReader-QG [67], DuReaderrobust-QG [68].”
Zero-Shot Cross-Lingual Neural Headline Generation (2018, methodology): “For the Chinese NHG model, the training set and development set all come from LCSTS [16].”
NCLS: Neural Cross-Lingual Summarization (2019, background): “Specifically, we propose a simple yet effective round-trip translation strategy to obtain cross-lingual documentsummary pairs from existing monolingual summarization datasets (Hermann et al., 2015; Zhu et al., 2018; Hu et al., 2015).”
Efficient Summarization with Read-Again and Copy Mechanism (2016, background): “Hu et al. (2015) build a large dataset for Chinese text summarization and propose to feed all hidden states from the encoder into the decoder.”
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking (2019, methodology): “As supervised baselines, we employ vanilla neural sequence-to-sequence models for abstractive summarization (Hu et al., 2015), following previous studies (Ma et al.”
Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention (2019, methodology): “Additionally, we also implement some baselines for comparison, some of which utilize a large corpus of Chinese short text summarization (LCSTS) (Hu et al., 2015), which is collected from the Chinese microblogging website Sina Weibo with 2.”
Neural Headline Generation with Sentence-wise Optimization (2016, methodology): “We also notice that MLE model is already better than (Hu et al., 2015) and comparable to (Gu et al., 2016).”
A Comprehensive Overview of Large Language Models (2023, neutral): “0 Chinese text corpora, Baidu Search, Web text, QA-long, QA-short, Poetry & Couplet Domain-specific data from medical, law and financial area Baidu knowledge graph with more than 50 million facts NLU: NLPCC2014-SC, SE-ABSA16_PHNS, SE-ABSA16_CAME, BDCI2019, COTE-BD [268], COTE-DP [268], COTE-MFW [268”
Global Encoding for Abstractive Summarization (2018, methodology): “We follow the previous research (Hu et al., 2015) to split the dataset for training, validation and testing, with 2.”
CPM-2: Large-scale Cost-effective Pre-trained Language Models (2021, methodology): “We utilize LCSTS (Hu et al., 2015) to evaluate the summarization capability.”