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Author
Date
2023Type
- Doctoral Thesis
ETH Bibliography
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Abstract
In scientific writing, retrieving, summarizing, and citing relevant papers is necessary but usually time-consuming. Recent research in natural language processing (NLP) has explored the use of neural networks to recommend, summarize and cite papers automatically. However, the following challenges remain before applying these NLP techniques to help authors write scientific articles. First, the rapidly growing volume of available scientific literature has raised the demand for accuracy and efficiency in recommending citations. Second, the length of scientific articles requires high memory efficiency in the summarization model, so long articles do not need to be truncated when summarizing them. Third, the generation of citation sentences needs to be well-controllable to allow authors to direct the generation as they wish. In addition, there is a lack of an integrated system that allows users to search for papers, obtain paper summaries, and get suggested citation sentences to cite them, all in a one-stop shop.
In this thesis, we aim to develop an integrated system for joint paper retrieval, summarization, and citation generation, which consists of the following four parts.
In the first part, to balance speed and accuracy, we propose a two-stage citation recommendation system that first prefetches K candidate papers by embedding-based K-nearest neighbor search and then reranks the prefetched papers with a fine-tuned SciBERT.
In the second part, we develop a reinforcement learning-based sentence extraction model that summarizes a document by iteratively scoring sentences based on the extraction history (e.g., which sentences were selected) and selecting the highest-scoring sentence. Moreover, the lightweight structure allows our model to summarize long scientific articles efficiently and surpass previous state-of-the-art BERT-based extractive summarizers.
In the third part, we propose a controllable citation generation model that users can control by specifying citation attributes. We define the citation attributes as the intent of the citation (e.g., to introduce context or to compare results), the keywords that the user expects to appear in the citation or the specific sentences in the body of the cited paper that are most relevant to the expected citation sentences.
In the final part, we integrate the subsystems for paper retrieval, summarization, and citation generation into a convenient user interface that displays recommended papers, extracted summaries of recommended papers, and abstractively generated citation sentences that are consistent with the context and selected keywords.
Our work is a step toward applying NLP techniques to help authors write academic papers in real-life scenarios and one of the early attempts at artificial intelligence (AI)-driven scientific inference. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000607048Publication status
publishedExternal links
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Publisher
ETH ZurichSubject
Information Retrieval; Summarization; Text GenerationOrganisational unit
03774 - Hahnloser, Richard H.R. / Hahnloser, Richard H.R.
Funding
182638 - The roles of vocal communication in pair formation and cultural learning in songbirds (SNF)
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ETH Bibliography
yes
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