Update README.md

README.md

@@ -27,54 +27,48 @@ pretty_name: SAMSum Corpus
 # Dataset Card for SAMSum Corpus
 ## Dataset Description
 ### Links
-- **Homepage:** https://aclanthology.org/2021.findings-acl.449
-- **Repository:** https://github.com/cylnlp/dialogsum
-- **Paper:** https://aclanthology.org/2021.findings-acl.449
+- **Homepage:** hhttps://arxiv.org/abs/1911.12237v2
+- **Repository:** https://arxiv.org/abs/1911.12237v2
+- **Paper:** https://arxiv.org/abs/1911.12237v2
 - **Point of Contact:** https://huggingface.co/knkarthick
 
 ### Dataset Summary
-DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics.
+The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person.
+The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0).
+
 ### Languages
 English
 
 ## Dataset Structure
 ### Data Instances
-DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues split into train, test and validation.
+SAMSum dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people
 The first instance in the training set:
-{'id': 'train_0', 'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", 'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", 'topic': "get a check-up}
+{'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked  cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"}
+
 ### Data Fields
 - dialogue: text of dialogue.
-- summary: human written summary of the dialogue.
-- topic: human written topic/one liner of the dialogue.
+- summary: one liner human written summary of the dialogue.
 - id: unique file id of an example.
 
 ### Data Splits
-- train: 12460
-- val: 1500
-- test: 1500
-- holdout: 100 [Only 3 features: id, dialogue, topic]
+- train: 14732
+- val: 818
+- test: 819
 
 ## Dataset Creation
 ### Curation Rationale
 In paper:
-We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers.
-
-Compared with previous datasets, dialogues from DialogSum have distinct characteristics:
+In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assistant and a client buying petrol.
+As a consequence, we decided to create a chat dialogue dataset by constructing such conversations that would epitomize the style of a messenger app.
 
-Under rich real-life scenarios, including more diverse task-oriented scenarios;
-Have clear communication patterns and intents, which is valuable to serve as summarization sources;
-Have a reasonable length, which comforts the purpose of automatic summarization.
-
-We ask annotators to summarize each dialogue based on the following criteria:
-Convey the most salient information;
-Be brief;
-Preserve important named entities within the conversation;
-Be written from an observer perspective;
-Be written in formal language.
 ### Who are the source language producers?
 linguists
 ### Who are the annotators?
 language experts
+### Annotation process
+In paper:
+Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one reference summary.
+
 
 ## Licensing Information
 non-commercial licence: CC BY-NC-ND 4.0