KPDROP: Improving Absent Keyphrase Generation

Ray, Chowdhury, Jishnu; Yeon, Park, Seo; Kundu, Tuhin; Caragea, Cornelia

doi:10.18653/v1/2022.findings-emnlp.357

Cited by 2 publications

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“…The task of keyphrase generation is introduced to predict both present and absent keyphrases. (Swaminathan et al, 2020), hierarchical decoding (Chen et al, 2020b), graphs (Ye et al, 2021a), dropout (Ray Chowdhury et al, 2022), and pretraining (Kulkarni et al, 2022;Wu et al, 2022a) to improve keyphrase generation. Furthermore, there have been several attempts to unify KE and KG tasks into a single learning framework.…”

Section: Keyphrase Generationmentioning

confidence: 99%

“…Following previous work (Meng et al, 2021;Ray Chowdhury et al, 2022), we measure the upper bound performance after overgeneration by calculating the recall score of the generated phrases and report the results in Table 6. The high recall demonstrates the potential for reranking to increase precision, and we observe that there is room for improvement by better reranking, opening up an opportunity for future research.…”

Section: Upper Bound Performancementioning

confidence: 99%

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Strategy of Naver Webtoon : News article analysis by period

Choi¹,

Kim²

2022

ISM

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Keyphrase generation (KG) aims to generate a set of summarizing words or phrases given a source document, while keyphrase extraction (KE) aims to identify them from the text. Because the search space is much smaller in KE, it is often combined with KG to predict keyphrases that may or may not exist in the corresponding document. However, current unified approaches adopt sequence labeling and maximization-based generation that primarily operate at a token level, falling short in observing and scoring keyphrases as a whole. In this work, we propose SIMCKP, a simple contrastive learning framework that consists of two stages: 1) An extractor-generator that extracts keyphrases by learning context-aware phraselevel representations in a contrastive manner while also generating keyphrases that do not appear in the document; 2) A reranker that adapts scores for each generated phrase by likewise aligning their representations with the corresponding document. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed approach, which outperforms the state-of-the-art models by a significant margin. IntroductionKeyphrase prediction (KP) is a task of identifying a set of relevant words or phrases that capture the main ideas or topics discussed in a given document. Prior studies have defined keyphrases that appear in the document as present keyphrases and the opposites as absent keyphrases. High-quality keyphrases are beneficial for various applications such as information retrieval (Kim et al., 2013), text summarization (Pasunuru and Bansal, 2018), and translation (Tang et al., 2016). KP methods are generally divided into keyphrase extraction (KE) (Witten * Work done during an internship at Naver Webtoon.

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