Exploring Sentence Vector Spaces through Automatic Summarization
Given vector representations for individual words, it is necessary to compute vector representations of sentences for many applications in a compositional manner, often using artificial neural networks. Relatively little work has explored the internal structure and properties of such sentence vectors. In this paper, we explore the properties of sentence vectors in the context of automatic summarization. In particular, we show that cosine similarity between sentence vectors and document vectors is strongly correlated with sentence importance and that vector semantics can identify and correct gaps between the sentences chosen so far and the document. In addition, we identify specific dimensions which are linked to effective summaries. To our knowledge, this is the first time specific dimensions of sentence embeddings have been connected to sentence properties. We also compare the features of different methods of sentence embeddings. Many of these insights have applications in uses of sentence embeddings far beyond summarization.
Code (0)
등록된 구현이 없습니다.
Tasks
SentenceSentence EmbeddingsSimilar Papers 제목 키워드 기반
Exploring Sentence Vectors Through Automatic Summarization
Vector semantics, especially sentence vectors, have recently been used successfully in many areas of natural language processing. However, relatively little work has explored the internal structure and properties of spac…
SentenceSentence EmbeddingsSentence Analogies: Exploring Linguistic Relationships and Regularities in Sentence Embeddings
While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations. Word vectors are often evaluated by assessing to what de…
SentenceSentence EmbeddingSentence-EmbeddingSentence EmbeddingsPutting Words in BERT’s Mouth: Navigating Contextualized Vector Spaces with Pseudowords
We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond to word senses. By inducing a contextua…
SentenceExploring Semantic Properties of Sentence Embeddings
Neural vector representations are ubiquitous throughout all subfields of NLP. While word vectors have been studied in much detail, thus far only little light has been shed on the properties of sentence embeddings. In thi…
Machine TranslationReading ComprehensionSemantic Textual SimilaritySentence+3Putting Words in BERT's Mouth: Navigating Contextualized Vector Spaces with Pseudowords
We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond to word senses. By inducing a contextua…
Sentence