Bottom-Up Abstractive Summarization

Gehrmann, Sebastian; Deng, Yuntian; Rush, Alexander M.

doi:10.48550/arxiv.1808.10792

Cited by 40 publications

(53 citation statements)

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“…The 'OpenNMT BRNN (2 layer, emb 256, hid 1024)' pre-trained model 4 has been used. • CopyTransformer (Gehrmann et al, 2018) 5 .…”

Section: Abstractive Summarisation Methodologymentioning

confidence: 99%

“…We have carried out an evaluation with 6 abstractive summarisation models: BART (Lewis et al, 2019), T5 (Raffel et al, 2019), BERT (PreSumm -BertSumExtAbs: Liu and Lapata, 2019), PG (Pointer-Generator with Coverage Penalty) (See et al, 2017), CopyTransformer (Gehrmann et al, 2018), and FastAbsRL (Chen and Bansal, 2018). Those models are applied in combination with the machine translation system MarianMT (Junczys-Dowmunt et al, 2018) using the Opus-MT models (Tiedemann and Thottingal, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes

Arana-Catania¹,

Procter²,

He³

et al. 2021

Preprint

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We present work on summarising deliberative processes for non-English languages. Unlike commonly studied datasets, such as news articles, this deliberation dataset reflects difficulties of combining multiple narratives, mostly of poor grammatical quality, in a single text. We report an extensive evaluation of a wide range of abstractive summarisation models in combination with an off-the-shelf machine translation model. Texts are translated into English, summarised, and translated back to the original language. We obtain promising results regarding the fluency, consistency and relevance of the summaries produced. Our approach is easy to implement for many languages for production purposes by simply changing the translation model.

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“…The 'OpenNMT BRNN (2 layer, emb 256, hid 1024)' pre-trained model 4 has been used. • CopyTransformer (Gehrmann et al, 2018) 5 .…”

Section: Abstractive Summarisation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes

Arana-Catania¹,

Procter²,

He³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Very recently, GEDI (Krause et al, 2020) achieves strong performance by using CCLM generators as discriminators, though it relies on several heuristics. More broadly, text generation models for style transfer (Hu et al, 2017;Lample et al, 2018b;Dai et al, 2019a), summarization (See et al, 2017;Gehrmann et al, 2018;Zaheer et al, 2020), and machine translation (Lample et al, 2018a;Ng et al, 2019;Lewis et al, 2019) can also be viewed as CCLM's for different "attributes. "…”

Section: Related Workmentioning

confidence: 99%

FUDGE: Controlled Text Generation With Future Discriminators

Yang,

Klein

2021

Preprint

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We propose Future Discriminators for Generation (FUDGE), a flexible and modular method for controlled text generation. Given a preexisting model G for generating text from a distribution of interest, FUDGE enables conditioning on a desired attribute a (for example, formality) while requiring access only to G's output logits. FUDGE learns an attribute predictor operating on a partial sequence, and uses this predictor's outputs to adjust G's original probabilities. We show that FUDGE models terms corresponding to a Bayesian decomposition of the conditional distribution of G given attribute a. Moreover, FUDGE can easily compose predictors for multiple desired attributes. We evaluate FUDGE on three tasks -couplet completion in poetry, topic control in language generation, and formality change in machine translation -and observe gains in all three tasks.

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“…Ptr-Net model also adds coverage loss, which examines the difference between the attentions of previous words generated and the current attention, in an attempt to fix the issue of word repetition, a persistent issue in seq2seq models. Gehrmann et al [7] try to improve the fluency of the generated text through various constraints applied during model training. Soft constraints on the size of text are used to constrain the length of generated descriptions, while constraints on the output probability distribution of words ameliorates word repetition.…”

Section: Related Workmentioning

confidence: 99%

Generating Rich Product Descriptions for Conversational E-commerce Systems

Kedia

Mantha

Gupta

et al. 2021

Companion Proceedings of the Web Conference 2021

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Through recent advancements in speech technologies and introduction of smart assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number of users are interacting with various applications through voice commands. E-commerce companies typically display short product titles on their webpages, either human-curated or algorithmically generated, when brevity is required. However, these titles are dissimilar from natural spoken language. For example, "Lucky Charms Gluten Free Break-fast Cereal, 20.5 oz a box Lucky Charms Gluten Free" is acceptable to display on a webpage, while a similar title cannot be used in a voice based text-to-speech application. In such conversational systems, an easy to comprehend sentence, such as "a 20.5 ounce box of lucky charms gluten free cereal" is preferred. Compared to display devices, where images and detailed product information can be presented to users, short titles for products which convey the most important information, are necessary when interfacing with voice assistants. We propose eBERT, a sequence-to-sequence approach by further pre-training the BERT embeddings on an e-commerce product description corpus, and then fine-tuning the resulting model to generate short, natural, spoken language titles from input web titles. Our extensive experiments on a real-world industry dataset, as well as human evaluation of model output, demonstrate that eBERT summarization outperforms comparable baseline models.Owing to the efficacy of the model, a version of this model has been deployed in real-world setting.

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Bottom-Up Abstractive Summarization

Cited by 40 publications

References 0 publications

Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes

Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes

FUDGE: Controlled Text Generation With Future Discriminators

Generating Rich Product Descriptions for Conversational E-commerce Systems

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