# Title-Guided Encoding for Keyphrase Generation

arXiv: https://arxiv.org/abs/1808.08575 · HTML: https://mlsanity.com/paper/1808.08575/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2018.

## Built on by (most cited first, 30 of 36)

- [One2Set: Generating Diverse Keyphrases as a Set](https://mlsanity.com/paper/2105.11134/) (2021)
- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training](https://mlsanity.com/paper/2205.10471/) (2022)
- [UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction](https://mlsanity.com/paper/2106.04847/) (2021)
- [Heterogeneous Graph Neural Networks for Keyphrase Generation](https://mlsanity.com/paper/2109.04703/) (2021)
- [Representation Learning for Resource-Constrained Keyphrase Generation](https://mlsanity.com/paper/2203.08118/) (2022)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning](https://mlsanity.com/paper/2104.08799/) (2021)
- [Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation](https://mlsanity.com/paper/2201.05302/) (2022)
- [WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation](https://mlsanity.com/paper/2211.06862/) (2022)
- [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://mlsanity.com/paper/2212.10233/) (2022)
- [ChatGPT vs State-of-the-Art Models: A Benchmarking Study in Keyphrase Generation Task](https://mlsanity.com/paper/2304.14177/) (2023)
- [Keyphrase Generation Beyond the Boundaries of Title and Abstract](https://mlsanity.com/paper/2112.06776/) (2021)
- [Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2310.06374/) (2023)
- [Unsupervised Deep Keyphrase Generation](https://mlsanity.com/paper/2104.08729/) (2021)
- [KPDrop: Improving Absent Keyphrase Generation](https://mlsanity.com/paper/2112.01476/) (2021)
- [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://mlsanity.com/paper/2211.12124/) (2022)
- [General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation](https://mlsanity.com/paper/2208.09606/) (2022)
- [From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning](https://mlsanity.com/paper/2408.08981/) (2024)
- [SGG: Learning to Select, Guide, and Generate for Keyphrase Generation](https://mlsanity.com/paper/2105.02544/) (2021)
- [Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production](https://mlsanity.com/paper/2302.09300/) (2023)
- [SimCKP: Simple Contrastive Learning of Keyphrase Representations](https://mlsanity.com/paper/2310.08221/) (2023)
- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts](https://mlsanity.com/paper/2409.13266/) (2024)
- [Hashtag-Guided Low-Resource Tweet Classification](https://mlsanity.com/paper/2302.10143/) (2023)
- [On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation](https://mlsanity.com/paper/2402.14052/) (2024)
- [GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation](https://mlsanity.com/paper/2409.03140/) (2024)
- [Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering](https://mlsanity.com/paper/2309.04734/) (2023)
- [LLMDistill4Ads: Using Cross-Encoders to Distill from LLM Signals for Advertiser Keyphrase Recommendations at eBay](https://mlsanity.com/paper/2508.03628/) (2025)
- [Attend and select: A segment selective transformer for microblog hashtag generation](https://mlsanity.com/paper/2106.03151/) (2021)

## Abstract (arXiv)

Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoder-decoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a title-guided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios.