A Comprehensive Attempt to Research Statement Generation
arXiv:2104.14339
Abstract
For a researcher, writing a good research statement is crucial but costs a lot of time and effort. To help researchers, in this paper, we propose the research statement generation (RSG) task which aims to summarize one's research achievements and help prepare a formal research statement. For this task, we conduct a comprehensive attempt including corpus construction, method design, and performance evaluation. First, we construct an RSG dataset with 62 research statements and the corresponding 1,203 publications. Due to the limitation of our resources, we propose a practical RSG method which identifies a researcher's research directions by topic modeling and clustering techniques and extracts salient sentences by a neural text summarizer. Finally, experiments show that our method outperforms all the baselines with better content coverage and coherence.
References in corpus (12)
- BERTScore: Evaluating Text Generation with BERT
- Discovering Discrete Latent Topics with Neural Variational Inference
- Learning Determinantal Point Processes
- MeanSum: A Neural Model for Unsupervised Multi-document Abstractive Summarization
- Generating Wikipedia by Summarizing Long Sequences
- Graph-based Neural Multi-Document Summarization
- Hierarchical Transformers for Multi-Document Summarization
- Towards a Neural Network Approach to Abstractive Multi-Document Summarization
- Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
- Leveraging Graph to Improve Abstractive Multi-Document Summarization
- Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization
- Scientific Article Summarization Using Citation-Context and Article's Discourse Structure