Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity

• URL: http://arxiv.org/abs/2111.08366v1
• Date: Tue, 16 Nov 2021 11:12:30 GMT
• Title: Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity
• Authors: Sheshera Mysore, Arman Cohan, Tom Hope
• Abstract summary: We present a new scientific document similarity model based on matching fine-grained aspects. Our model is trained using co-citation contexts that describe related paper aspects as a novel form of textual supervision.
• Score: 11.157086694203201
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present Aspire, a new scientific document similarity model based on matching fine-grained aspects. Our model is trained using co-citation contexts that describe related paper aspects as a novel form of textual supervision. We use multi-vector document representations, recently explored in settings with short query texts but under-explored in the challenging document-document setting. We present a fast method that involves matching only single sentence pairs, and a method that makes sparse multiple matches with optimal transport. Our model improves performance on document similarity tasks across four datasets. Moreover, our fast single-match method achieves competitive results, opening up the possibility of applying fine-grained document similarity models to large-scale scientific corpora.

Related papers

• Unified Multi-Modal Interleaved Document Representation for Information Retrieval [57.65409208879344]
  We produce more comprehensive and nuanced document representations by holistically embedding documents interleaved with different modalities. Specifically, we achieve this by leveraging the capability of recent vision-language models that enable the processing and integration of text, images, and tables into a unified format and representation.
  arXiv  Detail & Related papers  (2024-10-03T17:49:09Z)
• XDoc: Unified Pre-training for Cross-Format Document Understanding [84.63416346227176]
  XDoc is a unified pre-trained model which deals with different document formats in a single model. XDoc achieves comparable or even better performance on a variety of downstream tasks compared with the individual pre-trained models.
  arXiv  Detail & Related papers  (2022-10-06T12:07:18Z)
• Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval [79.37614949970013]
  We propose a new dense retrieval model which learns diverse document representations with deep query interactions. Our model encodes each document with a set of generated pseudo-queries to get query-informed, multi-view document representations.
  arXiv  Detail & Related papers  (2022-08-08T16:00:55Z)
• Large-Scale Multi-Document Summarization with Information Extraction and Compression [31.601707033466766]
  We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents. Our framework processes documents telling different stories instead of documents on the same topic. Our experiments demonstrate that our framework outperforms current state-of-the-art methods in this more generic setting.
  arXiv  Detail & Related papers  (2022-05-01T19:49:15Z)
• Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings [46.324584649014284]
  A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. This paper introduces a new topic-modeling framework where each document is viewed as a set of word embedding vectors and each topic is modeled as an embedding vector in the same embedding space. Embedding the words and topics in the same vector space, we define a method to measure the semantic difference between the embedding vectors of the words of a document and these of the topics, and optimize the topic embeddings to minimize the expected difference over all documents.
  arXiv  Detail & Related papers  (2022-03-03T08:46:23Z)
• Modeling Endorsement for Multi-Document Abstractive Summarization [10.166639983949887]
  A crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s) In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization. Our method generates a synopsis from each document, which serves as an endorser to identify salient content from other documents.
  arXiv  Detail & Related papers  (2021-10-15T03:55:42Z)
• Automatic Document Sketching: Generating Drafts from Analogous Texts [44.626645471195495]
  We introduce a new task, document sketching, which involves generating entire draft documents for the writer to review and revise. These drafts are built from sets of documents that overlap in form - sharing large segments of potentially reusable text - while diverging in content. We investigate the application of weakly supervised methods, including use of a transformer-based mixture of experts, together with reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-14T06:46:06Z)
• Aspect-based Document Similarity for Research Papers [4.661692753666685]
  We extend similarity with aspect information by performing a pairwise document classification task. We evaluate our aspect-based document similarity for research papers. Our results show SciBERT as the best performing system.
  arXiv  Detail & Related papers  (2020-10-13T13:51:21Z)
• Leveraging Graph to Improve Abstractive Multi-Document Summarization [50.62418656177642]
  We develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries.
  arXiv  Detail & Related papers  (2020-05-20T13:39:47Z)
• Explaining Relationships Between Scientific Documents [55.23390424044378]
  We address the task of explaining relationships between two scientific documents using natural language text. In this paper we establish a dataset of 622K examples from 154K documents.
  arXiv  Detail & Related papers  (2020-02-02T03:54:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.