---
language:
- en
license: apache-2.0
size_categories:
- 1K<n<10K
source_datasets: tau/scrolls
task_categories:
- text2text-generation
- summarization
tags:
- scrolls
- qmsum
dataset_info:
- config_name: default
  features:
  - name: id
    dtype: string
  - name: pid
    dtype: string
  - name: input
    dtype: string
  - name: output
    dtype: string
  - name: input_token_count
    dtype: int64
  - name: output_token_count
    dtype: int64
  splits:
  - name: train
    num_bytes: 68960760
    num_examples: 1257
  - name: validation
    num_bytes: 15700972
    num_examples: 272
  - name: test
    num_bytes: 16120860
    num_examples: 281
  download_size: 42316972
  dataset_size: 100782592
- config_name: no-prefix
  features:
  - name: id
    dtype: string
  - name: pid
    dtype: string
  - name: input
    dtype: string
  - name: output
    dtype: string
  - name: prompt
    dtype: string
  splits:
  - name: train
    num_bytes: 68944419
    num_examples: 1257
  - name: validation
    num_bytes: 15697436
    num_examples: 272
  - name: test
    num_bytes: 16117207
    num_examples: 281
  download_size: 6180898
  dataset_size: 100759062
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
- config_name: no-prefix
  data_files:
  - split: train
    path: no-prefix/train-*
  - split: validation
    path: no-prefix/validation-*
  - split: test
    path: no-prefix/test-*
---
# qmsum-cleaned



## prefixes


It's worth noting that each "document" in `input` is prefixed by a question/prompt on what the model is supposed to do. **You may want to explicitly handle this in some way, or prefix your models trained on this dataset.**


Most frequent "prefixes" separated via [sentence-splitter](https://github.com/mediacloud/sentence-splitter) in the `train` split:

|    | Sentence                                                                      |   Count |
|---:|:------------------------------------------------------------------------------|--------:|
|  0 | Summarize the whole meeting.                                                  |     121 |
|  1 | Summarize the meeting                                                         |      25 |
|  2 | What did the team discuss about the product cost?                             |       4 |
|  3 | How did Marketing design the product evaluation?                              |       4 |
|  4 | Summarize the wrap up of the meeting.                                         |       3 |
|  5 | What did the group discuss about user requirements of the new remote control? |       3 |
|  6 | What did the team discuss during the product evaluation?                      |       3 |
|  7 | Summarize the meeting.                                                        |       2 |
|  8 | Summarize what was said about digits form                                     |       2 |
|  9 | What was discussed in the meeting?                                            |       2 |

### wordcloud

Visualized as a wordcloud (`train` split):

![wc](prefix-train-wordcloud.png)

## token counts

![counts](https://i.imgur.com/rARAOvr.png)