You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Document Encoder. Our document encoder has a very similar architecture. We first segment a document d into its constituent tokens d_{1}d_{2}...d_{m}, to which we prepend BERT’s start token [CLS] followed by our special token [D] that indicates a document sequence. Unlike queries, we do not append [mask] tokens to documents. After passing this input sequence through BERT and the subsequent linear layer, the document encoder filters out the embeddings corresponding to punctuation symbols, determined via a pre-defined list. This filtering is meant to reduce the number of embeddings per document, as we hypothesize that (even contextualized) embeddings of punctuation are unnecessary for effectiveness. 

Question: Targeting memory-efficient indexing, can we also prune out redundant tokens in documents while preserving a sufficient level of fine granularity?
Targeting memory-efficient indexing, tokens are not appended in documents. We first segment a document d into its constituent tokens d_{1}d_{2}...d_{m}, to which we prepend BERT’s start token [CLS] followed by our special token [D] that indicates a document sequence. Unlike queries, we do not append [mask] tokens to documents. After passing this input sequence through BERT and the subsequent linear layer, the document encoder filters out the embeddings corresponding to punctuation symbols, determined via a pre-defined list. This filtering is meant to reduce the number of embeddings per document, as we hypothesize that (even contextualized) embeddings of punctuation are unnecessary for effectiveness.