Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content. To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. We develop a novel NLG model to produce a target sequence based on a given list of entities. Our model has a multi- step decoder that injects the entity types into the process of entity mention generation. Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines.

Injecting Entity Types into Entity-Guided Text Generation

**Read the paper on the folowing link:**

https://www.aclweb.org/anthology/2021.naacl-main.474/

**Abstract:**

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MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization

Automatic abstractive summaries are found to often distort or fabricate facts in the article. This inconsistency between summary and original text has seriously impacted its applicability. We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems. Empirical results show that the fact-aware summarization can produce abstractive summaries with higher factual consistency compared with existing systems, and the correction model improves the factual consistency of given summaries via modifying only a few keywords.

Enhancing Factual Consistency of Abstractive Summarization

Virtual Poster Session II: Generation

poster

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.



<i>**Session Chair: ** Yang Liu </i><br><br>

16E-Oral: Summarization

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Chenguang Zhu

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SHORT BIO

Presentations

Injecting Entity Types into Entity-Guided Text Generation

MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization

Enhancing Factual Consistency of Abstractive Summarization

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