Update README.md

# AOSED — Arabic Opinion Summary Evaluation Dataset

AOSED is the first evaluation benchmark for **Arabic opinion summarization**. It pairs
Arabic customer reviews of 100 restaurants with human-validated reference summaries,
summaries generated by three large language models, and human annotations of summary
quality.

The dataset accompanies the paper *ArabicOpinion: A Five-Dimensional Multi-Granular
Evaluation Framework for Arabic Opinion Summarization*, published in **ACM Transactions
on Asian and Low-Resource Language Information Processing (TALLIP)**.

## What is in it

| | Count |
|---|---|
| Restaurants | 100 |
| Customer reviews | 1,944 (10–27 per restaurant, mean 19.44) |
| Human-validated reference summaries | 100 |
| LLM-generated summaries | 300 (GPT-4o, Claude, JAIS) |
| Annotated summaries | 300, each scored on relevance, faithfulness, and sentiment |

Exact unit counts for every table are in `statistics.csv`.

## Files

**`restaurants.csv`** — `place_id`, `average_rating`

**`reviews.csv`** — `place_id`, `review_id`, `review_text`
One row per review. Text only; no reviewer names, usernames, or identifiers.

**`gold_summaries.csv`** — `place_id`, `golden_summary`, `sentiment_gold`, `n_reviews`,
`n_opinions`, `n_entities`, `n_opinion_units_extracted`, `n_entity_units_extracted`

`n_opinions` and `n_entities` are the counts recorded by the annotators; these are the
denominators used to compute the published relevance scores. The `_extracted` columns are
the counts recoverable from the unit text. They agree for 98 of 100 restaurants; in the two exceptions
the difference is a single unit.

**`gold_units.csv`** — `place_id`, `unit_id`, `unit_type`, `unit_text`, `polarity`
Opinion phrases and named entities extracted from each reference summary. `unit_type` is
`opinion` or `entity`. `polarity` is `positive`, `negative`, or empty where not annotated.

**`gold_facts.csv`** — `place_id`, `fact_id`, `fact_text`
Objective, non-evaluative statements extracted from the reference summaries. Included for
completeness; the evaluation framework scores opinions and entities only.

**`system_summaries.csv`** — `place_id`, `model`, `summary`, `sentiment_system`,
`n_repetitions`, `n_conflicts`
`n_repetitions` and `n_conflicts` are annotator counts of repeated and mutually
contradictory opinions within a summary.

**`system_units.csv`** — `place_id`, `model`, `unit_id`, `unit_type`, `unit_text`

**`annotations.csv`** — `place_id`, `model`, `relevance`, `faithfulness`
Final human scores in [0, 1]. **Relevance** is the proportion of reference opinions and
entities covered by the system summary. **Faithfulness** is the proportion of system
opinions and entities supported by the reference summary.

**`aosed_full.csv`** — a flat 300-row join of the above, for convenience.

**`statistics.csv`**— dataset statistics.

README.md

@@ -1,3 +1,19 @@
 ---
 license: cc-by-nc-sa-4.0
----
+task_categories:
+- summarization
+- text-generation
+- text-classification
+language:
+- ar
+tags:
+- opinion-summarization
+- arabic-nlp
+- summarization-evaluation
+- sentiment-analysis
+- llm-evaluation
+- benchmark
+pretty_name: AOSED — Arabic Opinion Summary Evaluation Dataset
+size_categories:
+- 1K<n<10K
+---