# BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation

arXiv: https://arxiv.org/abs/1909.09485 · HTML: https://mlsanity.com/paper/1909.09485/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [KeyGen2Vec: Learning Document Embedding via Multi-label Keyword Generation in Question-Answering](https://mlsanity.com/paper/2310.19650/) (2023)

## Abstract (arXiv)

This study mainly investigates two common decoding problems in neural keyphrase generation: sequence length bias and beam diversity. To tackle the problems, we introduce a beam search decoding strategy based on word-level and ngram-level reward function to constrain and refine Seq2Seq inference at test time. Results show that our simple proposal can overcome the algorithm bias to shorter and nearly identical sequences, resulting in a significant improvement of the decoding performance on generating keyphrases that are present and absent in source text.