Learning Rich Representation of Keyphrases from Text

Abstract

In this work, we explore how to train taskspecific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective -Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 8.16 points in F1) over SOTA, when the LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART -Key-BART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (upto 4.33 points in F1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition (NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks.

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

“…Song et al [217] evaluated ChatGPT on multiple datasets from news and scientific literature domains having both short and long documents. Experiment results showed that ChatGPT outperforms KeyBART [227], the SOTA model, on all the datasets.…”

“…The primary advantage of KPG over keyphrase extraction is the ability to generate both extractive and abstractive keyphrases. Keyphrase generation is approached as a sequence-to-sequence generation task [12], [226], [227] in the existing works. The current state-of-the-art model for keyphrase generation is, Key-BART [227], which is based on BART and trained using the text-to-text generation paradigm.…”

“…Keyphrase generation is approached as a sequence-to-sequence generation task [12], [226], [227] in the existing works. The current state-of-the-art model for keyphrase generation is, Key-BART [227], which is based on BART and trained using the text-to-text generation paradigm. Table 5 presents a summary of research works exploring GLLMs for keyphrase generation.…”

Strategy of Naver Webtoon : News article analysis by period

“…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.…”

MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction

“…Previous KPE works include supervised and unsupervised approaches. Supervised approaches model KPE as sequence tagging (Sahrawat et al, 2019;Alzaidy et al, 2019;Martinc et al, 2020;Santosh et al, 2020;Nikzad-Khasmakhi et al, 2021) or sequence generation tasks Kulkarni et al, 2021) and require large-scale annotated data to perform well. Since KPE annotations are expensive and largescale KPE annotated data is scarce, unsupervised KPE approaches, such as TextRank (Mihalcea and Tarau, 2004), YAKE (Campos et al, 2018), Em-bedRank (Bennani-Smires et al, 2018), are the mainstay in industry deployment.…”